Neuropsychology of Everyday Functioning (The Science and Practice of Neuropsychology)

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Neuropsychology of Everyday Functioning

The Science and Practice of Neuropsychology A Guilford Series Robert A. Bornstein, Series Editor Aphasia and Language: Theory to Practice Stephen E. Nadeau, Leslie J. Gonzalez Rothi, and Bruce A. Crosson, Editors

Cognitive and Behavioral Rehabilitation: From Neurobiology to Clinical Practice Jennie Ponsford, Editor

Developmental Motor Disorders: A Neuropsychological Perspective Deborah Dewey and David E. Tupper, Editors

The Human Frontal Lobes: Functions and Disorders, Second Edition Bruce L. Miller and Jeffrey L. Cummings, Editors

Neuropsychology of Everyday Functioning Thomas D. Marcotte and Igor Grant, Editors

Pediatric Neuropsychology: Research, Theory, and Practice, Second Edition Keith Owen Yeates, M. Douglas Ris, H. Gerry Taylor, and Bruce F. Pennington, Editors

Neuropsychology of Everyday Functioning Edited by

Thomas D. Marcotte Igor Grant

Series Editor’s Note by Robert A. Bornstein

THE GUILFORD PRESS New York   London

© 2010 The Guilford Press A Division of Guilford Publications, Inc. 72 Spring Street, New York, NY 10012 www.guilford.com All rights reserved No part of this book may be reproduced, translated, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, microfilming, recording, or otherwise, without written permission from the Publisher. Printed in the United States of America This book is printed on acid-free paper. Last digit is print number:  9  8  7  6  5  4  3  2  1 Library of Congress Cataloging-in-Publication Data Neuropsychology of everyday functioning / edited by Thomas D. Marcotte, Igor Grant.    p. cm.–(The science and practice of neuropsychology)   Includes bibliographical references and index.   ISBN 978-1-60623-459-4 (hbk.)   1.  Neuropsychology.  2.  Cognitive psychology.  3.  Cognitive neuroscience.  I.  Marcotte, Thomas D.  II.  Grant, Igor, 1942–   QP360.N4948 2010   612.8′2—dc22 2009016121

To Wendy, Kyle, and Kathryn, who prove that despite its day-to-day challenges, the real world is a wondrous place to be And to my parents, Bob and Carol, who always encouraged and supported me —T. D. M. To JoAnn Nallinger Grant, my partner in life —I. G.

About the Editors

Thomas D. Marcotte, PhD, is Associate Professor in the Department of Psychiatry at the University of California, San Diego (UCSD), and Center Manager of the HIV Neurobehavioral Research Center at UCSD. His research focuses on the development of methods for assessing and predicting the impact of cognitive impairments on the ability to carry out everyday activities, in particular, driving an automobile. Dr. Marcotte also has a program of research investigating HIV-related neurocognitive dysfunction, particularly in the international context. He has published numerous articles and book chapters on these topics and served on the editorial boards of the Journal of the International Neuropsychological Society and Neuropsychology. Igor Grant, MD, is Distinguished Professor of Psychiatry and Director of the HIV Neurobehavioral Research Center at the University of California, San Diego. He has contributed extensively to the literature on neuropsychiatry, particularly the effects of alcohol abuse, drug abuse, HIV, and other disease states on neurocognitive functioning and underlying brain disease. Dr. Grant’s work has also touched on the effects of life stress on health, in particular, physiological changes and coping among chronically stressed caregivers of patients with Alzheimer’s disease. He is Founding Editor of the Journal of the International Neuropsychological Society and AIDS and Behavior.



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Contributors

Amarilis Acevedo, PhD, ABPP/CN, Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Coral Gables, Florida Peter A. Arnett, PhD, Department of Psychology, Pennsylvania State University, University Park, Pennsylvania J. Hampton Atkinson, MD, Department of Psychiatry, University of California, San Diego, La Jolla, California Karlene Ball, PhD, Center for Research on Applied Gerontology, University of Alabama at Birmingham, Birmingham, Alabama Terry R. Barclay, PhD, Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California Carolyn M. Baum, PhD, OTR/L, FAOTA, Program in Occupation Therapy/Neurology, Washington University School of Medicine, St. Louis, Missouri Patricia Boyle, PhD, Rush Alzheimer’s Disease Center, Chicago, Illinois Mariana Cherner, PhD, Department of Psychiatry, University of California, San Diego, La Jolla, California Cara B. Fausset, MS, School of Psychology, Georgia Institute of Psychology, Atlanta, Georgia Igor Grant, MD, Department of Psychiatry and HIV Neurobehavioral Research Center, University of California, San Diego, La Jolla, California Michael F. Green, PhD, Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, and VA Greater Los Angeles Healthcare System Los Angeles, California Robert K. Heaton, PhD, ABPP/CN, Department of Psychiatry, University of California, San Diego, La Jolla, California Charles H. Hinkin, PhD, ABPP, Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, and VA Greater Los Angeles Healthcare System, Los Angeles, California Rujvi Kamat, BS, Joint Doctoral Program in Clinical Psychology, San Diego State University, San Diego, California

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Contributors

Robert M. Kaplan, PhD, Department of Health Services, University of California, Los Angeles, Los Angeles, California Noomi Katz, PhD, OTR, Research Institute for the Health and Medical Professions, Ono Academic College and School of Occupational Therapy, Hebrew University, Jerusalem, Israel Ida L. Kellison, MS, Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida Kristina Kowalski, MSc, Department of Psychology, University of Victoria, Victoria, British Columbia, Canada Rema A. Lillie, MSc, Department of Psychology, University of Victoria, Victoria, British Columbia, Canada David Loewenstein, PhD, Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Coral Gables, Florida Mark R. Lovell, PhD, Center for Sports Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania Paul Malloy, PhD, Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, Rhode Island Susan E. Maloney, MA, Department of Psychology, Division of Behavioral Neuroscience, University of Missouri–St. Louis, St. Louis, Missouri Thomas D. Marcotte, PhD, Department of Psychiatry and HIV Neurobehavioral Research Center, University of California, San Diego, La Jolla, California Catherine A. Mateer, PhD, RPsych, Department of Psychology, University of Victoria, Victoria, British Columbia, Canada Brent T. Mausbach, PhD, Department of Psychiatry, University of California, San Diego, La Jolla, California Andrew K. Mayer, MS, School of Psychology, Georgia Institute of Technology, Atlanta, Georgia Nicole C. R. McLaughlin, PhD, Department of Psychiatry and Human Behavior, Butler Hospital, Warren Alpert Medical School of Brown University, Providence, Rhode Island David J. Moore, PhD, Department of Psychiatry, University of California, San Diego, La Jolla, California Suzanne Moseley, BS, Veterans Medical Research Foundation, La Jolla, California Jamie E. Pardini, PhD, Center for Sports Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania Brigitte N. Patry, PhD, RPsych, Department of Psychology, Queen Elizabeth II Health Sciences Center, Halifax, Nova Scotia, Canada Thomas L. Patterson, PhD, Department of Psychiatry, University of California, San Diego, La Jolla, California Robert H. Paul, PhD, ABPP/ABCN, Department of Psychology, Division of Behavioral Neuroscience, University of Missouri–St. Louis, St. Louis, Missouri Matthew Rizzo, MD, Department of Neurology, Carver College of Medicine, University of Iowa, Iowa City, Iowa Wendy A. Rogers, PhD, School of Psychology, Georgia Institute of Technology, Atlanta, Georgia

Contributors

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Lesley A. Ross, PhD, Center for Research on Applied Gerontology, Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama Joseph R. Sadek, PhD, Department of Psychiatry, University of New Mexico, and Behavioral Health Care Line, New Mexico VA Health Care System, Albuquerque, New Mexico J. Cobb Scott, PhD, HIV Neurobehavioral Research Center, University of California, San Diego, La Jolla, California, and Department of Psychiatry, Yale University, New Haven, Connecticut Claire Sira, PhD, RPsych, Outpatient Neurorehabilitation Program, Victoria General Hospital, Victoria, British Columbia, Canada Megan M. Smith, PhD, Department of Psychiatry, University of Iowa, Iowa City, Iowa Holly Tuokko, PhD, RPsych, Department of Psychology and Centre on Aging, University of Victoria, Victoria, British Columbia, Canada Wilfred G. van Gorp, PhD, Department of Psychiatry, Columbia Presbyterian Medical Center, New York, New York Sarah Viamonte, MA, Minneapolis VA Medical Center, Minneapolis, Minnesota Matthew J. Wright, PhD, Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California

Series Editor’s Note

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he field of neuropsychology has continued to evolve over the past six decades. The initial focus of the field on questions related to detection of cognitive dysfunction in various disorders, and the use of neurobehavioral investigations to better understand brain–behavior relationships, has now been amplified by an increased focus on taking neuropsychology out of the laboratory and into the real world. This emphasis on understanding the generalizability of laboratory phenomena to everyday functioning will be a critical next step as the field embraces the concepts of evidence-based science and practice. Until recently clinicians’ judgments about the real-world implications of their evaluations were predicated on presumptions about which cognitive abilities were central to the performance of activities of daily living. The increasing engagement of occupational therapists, neuropsychologists, speech and language pathologists, rehabilitation psychologists, and other disciplines in the direct examination of the link between the laboratory and daily life is an important step forward. In this volume, editors Thomas D. Marcotte and Igor Grant have assembled leaders from diverse perspectives that reflect the complexity of the issues that impact our understanding of the relationship of laboratory findings and daily function. For example, in Chapter 2, Wendy A. Rogers, Andrew K. Mayer, and Cara B. Fausset introduce the field of ergonomics and human factors, which has obvious implications for the study of everyday implications, but is a field to which most neuropsychologists have had little exposure. Similarly, Carolyn M. Baum and Noomi Katz, in Chapter 3, present the perspective of the discipline of occupational therapy, which has a robust tradition of helping patients adapt to their environments and vice versa. As Baum and Katz point out, and as many neuropsychologists know, occupational therapists have a distinct approach to cognitive assessment that is explicitly oriented to functional abilities. Section B of Part I of the volume focuses on approaches to the measurement of specific functional domains such as medication management and driving, as well as the relationship of neuropsychological status with vocational performance and instrumental activities of daily living. Part III addresses questions of everyday ­function in the setting of several common neurological and psychiatric disorders and normal aging, and also discusses the impact of cognitive impairment on quality of life.

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Series Editor’s Note

This volume is the fifth in the Guilford series The Science and Practice of Neuropsychology. The goal of this series is to integrate the scientific foundations and clinical applications of knowledge of brain–behavior relationships. In the modern era, which emphasizes translational research, the contributors to this volume exemplify the application of evidence from the research and clinical laboratory to the real world. Robert A. Bornstein, PhD

Preface Life is a neuropsychological test. —Robert K. H eaton

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avigating through daily life is a complex and dynamic process. We must constantly filter an overabundance of new information, prioritize minute-to-minute actions, attend to time-sensitive problems while deliberating on others, engage in risky activities (e.g., driving), track appointments and deadlines, interact with others, and change strategies as needed. The human brain, fortunately, is efficient and adaptive, and despite these challenges it is rare to experience significant failure on most common everyday tasks. But brain damage can profoundly affect these abilities, and even individuals with mild neurocognitive impairments can struggle in completing their day-to-day activities. Although our ability to predict performance in the “wild” from assessments in the controlled laboratory or clinic has grown over the decades, it nonetheless remains inadequate. The aims of this book are twofold: (1) to explore the rationale, theory, and practical aspects of assessing everyday functioning, and (2) to review the impact of key neurological and psychiatric conditions on the ability to complete real-world tasks. Our hope is to provide a volume that stimulates critical thinking regarding current methods and to foster future research. This book is divided into two major parts. Part I addresses general approaches to evaluating the relationship between cognition and everyday functioning. Numerous professions focus on this issue, yet there is often limited dialogue between the groups. Methodologies are sometimes comparable and, at other times, divergent. One goal of this book is to expose the reader to these various methods. We therefore begin the volume in Section A with overviews of the neuropsychological, human factors, and occupational therapy approaches to examining real-world functioning. These chapters include contributions from distinguished researchers who have also served as presidents of one of the organizations serving their profession (Robert K. Heaton, International Neuropsychological Society; Wendy A. Rogers, Human Factors and Ergonomics Society; Carolyn M. Baum, American Occupational Therapy Association). Section B consists of chapters addressing the theoretical bases and practical issues involved in assessing specific components of everyday functioning. For this volume,

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Preface

we selected four aspects of real-world functioning that are challenging but common: instrumental activities of daily living (IADLs), vocational functioning, medication management, and automobile driving. Given the increasing diversity of many societies, as well as the growing emphasis on international research, we also include a chapter focusing on cross-cultural issues in the assessment of functional abilities. Part II reviews the impact of specific neurological and psychiatric conditions on real-world performance. We begin by examining how neurocognitive impairments affect overall quality of life, and follow with a discussion of normal aging and everyday functioning. The remainder of Part II addresses conditions commonly seen in the clinic: dementia/mild cognitive impairment, vascular dementia, traumatic brain injury, sports injuries and concussion, multiple sclerosis, HIV-associated neurocognitive disorders, depression, and schizophrenia. Each chapter includes background on the condition of interest and a discussion of its effects on IADLs, vocational performance, medication management, and driving. In the final chapter, based on the material presented throughout the book, we provide our opinions regarding directions for future work on the prediction of everyday functioning from laboratory measures. There are a number of individuals we would like to thank. In seeking contributors to this book, it was readily apparent that although many investigators may publish a study or two on everyday functioning, only a limited number of researchers are dedicated to addressing the theoretical and methodological issues associated with the prediction of real-world performance. We were fortunate to find such individuals for this book and are grateful to the authors who contributed to this volume. Our knowledge and interest in the importance of using neuropsychological measures to predict real-world functioning grows in part from our collaborations with Robert Heaton, PhD. Bob is a long-time colleague (IG) and mentor/colleague (TDM). His decades-long emphasis on the real-world impact of brain dysfunction has kept this issue at the forefront throughout our association. Last, we’d like to thank Robert F. Bornstein, PhD, editor of The Science and Practice of Neuropsychology series, who originally proposed this volume; Margaret Ryan, whose expert editing made this a much better product; and especially Rochelle Serwator, our editor at The Guilford Press, who so patiently nurtured this book into existence.

Contents

PART I.  Assessment Concepts and Methods Section A.  Approaches to Assessing the Relationship between Cognition and Everyday Functioning   1. Neuropsychology and the Prediction of Everyday Functioning

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Thomas D. Marcotte, J. Cobb Scott, Rujvi Kamat, and Robert K. Heaton

  2. Understanding the Relevance of Human Factors/Ergonomics to Neuropsychology Practice and the Assessment of Everyday Functioning

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Wendy A. Rogers, Andrew K. Mayer, and Cara B. Fausset

  3. Occupational Therapy Approach to Assessing the Relationship between Cognition and Function

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Carolyn M. Baum and Noomi Katz

Section B.  Assessment of Specific Functional Abilities and Assessment Considerations   4. The Relationship between Instrumental Activities of Daily Living and Neuropsychological Performance

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David Loewenstein and Amarilis Acevedo

  5. The Prediction of Vocational Functioning from Neuropsychological Performance

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Joseph R. Sadek and Wilfred G. van Gorp

  6. Medication Management

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Terry R. Barclay, Matthew J. Wright, and Charles H. Hinkin

  7. The Brain on the Road

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Matthew Rizzo and Ida L. Kellison

  8. Considerations in the Cross-­Cultural Assessment of Functional Abilities

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Mariana Cherner

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Contents

PART II.  Everyday Impact of Normal Aging and Neuropsychiatric Disorders   9. The Impact of Cognitive Impairments on Health-­Related Quality of Life

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Robert M. Kaplan, Brent T. Mausbach, Thomas D. Marcotte, and Thomas L. Patterson

10. Normal Aging and Everyday Functioning

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Karlene Ball, Lesley A. Ross, and Sarah Viamonte

11. Everyday Functioning in Dementia and Mild Cognitive Impairment

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Paul Malloy and Nicole C. R. McLaughlin

12. Everyday Functioning in Vascular Dementia

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Robert H. Paul, Susan E. Maloney, and Patricia Boyle

13. Everyday Impact of Traumatic Brain Injury

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Rema A. Lillie, Kristina Kowalski, Brigitte N. Patry, Claire Sira, Holly Tuokko, and Catherine A. Mateer

14. Neuropsychological Assessment and Sports-­Related Mild Traumatic Brain Injury (Concussion)

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Mark R. Lovell and Jamie E. Pardini

15. Cognitive Functioning and Everyday Tasks in Multiple Sclerosis

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Peter A. Arnett and Megan M. Smith

16. Everyday Impact of HIV-Associated Neurocognitive Disorders

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J. Cobb Scott and Thomas D. Marcotte

17. The Influence of Depression on Cognition and Daily Functioning

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David J. Moore, Suzanne Moseley, and J. Hampton Atkinson

18. Cognition and Daily Functioning in Schizophrenia

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Michael F. Green

Future Directions in the Assessment of Everyday Functioning

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Thomas D. Marcotte and Igor Grant

Index

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PART I

Assessment Concepts and Methods

S e c tio n A

Approaches to Assessing the Relationship between Cognition and Everyday Functioning

Chapter 1

Neuropsychology and the Prediction of Everyday Functioning Thomas D. Marcotte, J. Cobb Scott, Rujvi Kamat, and Robert K. Heaton

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odern neuropsychology rose to prominence as a discipline during the middle of the 20th century, based on the ability of neuropsychologists, armed with a toolkit of cognitive, motor, and sensory tests, to help localize brain lesions and contribute to the diagnosis of neurological and neuropsychiatric conditions. Over the past few decades, the need to use cognitive tests for lesion localization has waned, as new imaging techniques have enabled clinicians to locate brain abnormalities with increasing sensitivity and accuracy. However, brain imaging is not a panacea, as commonly used techniques frequently are not helpful in diagnosing some neurological conditions (e.g., mild traumatic brain injury or early dementing processes), and imaging can lack some specificity, in that brain lesions can be seen in a large proportion of otherwise “normal” adults, especially as they age (de Leeuw et al., 2001). Neuropsychological assessment is still critical, however, if one wants to know the nature and severity of any behavioral manifestations that may result from brain abnormalities. Indeed, increasingly a primary reason for referrals for neuropsychological testing is to answer questions regarding the effects that brain alterations are likely to have on everyday functioning, such as the ability to be successful at work, live independently (Rabin, Barr, & Burton, 2005), handle finances, or drive an automobile. In addition to being a common clinical question (Chelune & Moehle, 1986; Heaton & Pendleton, 1981), it is particularly a focus in forensic referrals, where decisions on financial compensation may depend on estimates of a client’s functional levels, and in referrals that seek to identify treatment targets for rehabilitation efforts. The neuropsychological approach to assessment, in the psychological tradition, usually integrates results on tests that have been well standardized and carefully characterized in terms of reliability and validity. Such measures can be useful for tracking the effects of disease progression, as well as any beneficial effects of rehabilitation programs or treatment of the underlying brain abnormality. In addition, by delineat

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ing an individual’s cognitive deficits, as well as strengths, neuropsychologists aim to understand how these might impact functioning in day-to-day life. However, since the foundations of neuropsychology include lesion localization and clinical diagnosis (e.g., assessment of cognitive decline), the neuropsychologist typically uses measures originally designed to address these issues rather than the prediction of how an individual might function in everyday life, given a particular injury or decline (Chaytor & Schmitter-­Edgecombe, 2003). For example, measures such as the Stroop Color–Word Interference Test and Tower of London were not originally designed to be used as clinical measures (Burgess et al., 2006). These instruments later found their way into the clinical realm and have been used to help predict difficulties with everyday functioning primarily based on the assumption that they assess functions/constructs that are important to carrying out real-world activities. As an example, with regard to the Stroop, one might hypothesize that the ability to inhibit an automatic, overlearned response would, at times, be beneficial to the safe driving of an automobile, such as being able to withhold a reflex to press the brakes if a traffic light turns red when the driver is halfway through the intersection. The approach of predicting everyday functioning using neuropsychological measures designed for other purposes has been questioned, because it is not always clear how performance on basic abilities translates to behavior within the varying environments found in the real world (Goldstein, 1996). Indeed, despite many advances in neuroscience we still know surprisingly little about how the brain enables us to interact with the environment and organize everyday activities, even ostensibly simple actions such as cooking (Burgess et al., 2006). In response, investigators have developed new measures that have a strong neuropsychological bent but focus on cognitive constructs specifically hypothesized to relate to real-world performance—­ measures designed to assess more directly the abilities needed to carry out everyday tasks. In this chapter we review key issues in the assessment of everyday functioning, including factors that complicate the relationship between performance on laboratory tests and real-world performance. In addition, we briefly summarize the literature on the use of different types of neuropsychological measures to predict real-world performance. We limit our discussion regarding specific neuropsychological predictors and outcomes, as this aspect is covered in the chapters throughout this book.

Ecological Validity Originally coined by Brunswik (1955), the term “ecological validity” refers to whether the findings obtained within a controlled experiment or environment can be generalized to what we see in the real world, where the organism exhibits “free behavior in the open environment” (Franzen, 2000). With respect to neuropsychology, Sbordone (1996) defined ecological validity as the “functional and predictive relationship between the patient’s performance on a set of neuropsychological tests and the patient’s behavior in a variety of real world settings” (p.  15). (Although the term “real world” has been criticized as being nonspecific [Rogers, 2008] and suggesting that behavior in the lab does not count as “real-world” behavior [Goldstein, 1996],

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we find it useful to indicate the environment outside the confines of the laboratory/ clinic.) Veridicality and verisimilitude are two general approaches to ecological validity, as described by Franzen and Wilhelm (1996). Veridicality is “the extent to which test results reflect or can predict phenomena in the open environment” (p. 93). This usually involves using neuropsychological measures or combinations of measures to predict real-world performance (e.g., employment status). Most neuropsychological measures would fall into this category, because they do not directly measure everyday behaviors but do assess some basic requirements of such behaviors and therefore may predict functioning outside of the laboratory. Verisimilitude refers to the “the topographical similarity of the data collection method to a task in the free environment” (Franzen, 2000, p. 47). In other words, the test resembles a task people perform in everyday life, and the test is developed considering the theoretical relationship between the demands of the test procedures and the behavior that is being predicted. The tests more closely approximate everyday tasks, so the inferential leap from test performance to real-world performance can be made easily (Spooner & Pachana, 2006). In reality, of course, perfect verisimilitude is impossible, since one cannot completely replicate the environment in which the behavior of interest will ultimately take place (Goldstein, 1996). Furthermore, it would appear impossible to capture in a standardized task all important differences that people experience in the specific requirements of their everyday roles of shopper, parent, financial manager, and so on. In addition, a test based on verisimilitude is not necessarily ecologically valid (Chaytor & Schmitter-­Edgecombe, 2003), and several have yet to be validated with respect to their true, real-world counterparts (Rabin, Burton, & Barr, 2007). Verisimilitude is an increasingly popular approach, however, and a growing number of instruments that more closely resemble real-world tasks have become widely available. Examples of more commonly used tests include the Rivermead Behavioral Memory Test (Wilson, Cockburn, & Baddeley, 1985), Behavioral Assessment of the Dysexecutive Syndrome (Wilson, Alderman, Burgess, Emslie, & Evans, 1996), and the Test of Everyday Attention (Robertson, Ward, Ridgeway, & Nimmo-Smith, 1996).

Examples Relating Neuropsychological Performance and Everyday Functioning A recurring question in the field is whether tests originally developed for detection and localization of brain pathology can predict real-world functioning (Heaton & Pendleton, 1981). Because of the importance of this question, a considerable amount of research has used traditional neuropsychological tests to predict outcomes such as academic performance, financial management, medication management, and automobile driving. Despite the fact that investigators have used a large variety of neuropsychological tests, ranging from a select number of measures to comprehensive batteries, and varying operational definitions of functional outcomes, it is clear that basic cognitive functioning (measured via neuropsychological tests) is related to one’s ability to carry out such real-world tasks. The strength of this relationship

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can best be characterized as “moderate.” Below we provide a few brief examples of this type of research; additional examples are provided in chapters throughout this volume.

Academic Achievement Intelligence tests were originally designed to predict academic achievement, and then secondarily were found to be sensitive to brain pathology. Numerous studies have examined the relationship between neurocognitive functioning and either concurrent or future academic success (most often defined as the number of years of schooling completed, or grades in school). There is generally a strong relationship between intelligence quotient (IQ) and academic success, with a correlation in the vicinity of .50 (Matarazzo, 1972; Sternberg, Grigorenko, & Bundy, 2001). IQ at age 7 has been found to be predictive of adult educational attainment (McCall, 1977), suggesting that, in general, cognitively more able people tend to succeed more and go further in school. Performance on IQ tests does not explain all of the variance seen in academic achievement, and many other factors may be important, such as home and school environment, parental expectations, self-­efficacy, and individual motivational levels, as well as abilities not assessed by IQ tests (e.g., learning efficiency and various “executive functions”). Matarazzo (1972) has argued that a minimum IQ threshold may be necessary to reach certain academic levels (e.g., high school diploma, graduate school), and that these additional factors may influence success beyond those levels. The interplay between IQ and education is complex, and the issue continues to be investigated with a variety of methods (Deary, Strand, Smith, & Fernandes, 2006; Rohde & Thompson, 2006). Regardless, since academic functioning in neurological patients can be impacted by neuropsychological deficits beyond pre- and postmorbid changes in IQ, a comprehensive neuropsychological test battery is recommended if one wants to determine the full impact of neurological conditions (Heaton & Pendleton, 1981).

Instrumental Activities of Daily Functioning Activities of daily living (ADLs) have generally been divided into two types: basic ADLs, comprised of activities such as grooming, dressing, feeding, toileting, and bathing, and instrumental ADLs (IADLs), which involve more complex tasks such as money management, shopping, medication management, and handling transportation needs. Basic ADLs are often significantly impacted by physical impairments, but clinicians should remain cognizant that physical impairments may affect IADL success as well. Neuropsychologists are most commonly asked to predict IADL functioning since the capacity to execute basic ADLs is more clinically apparent. Neuropsychological performance has been associated with IADL abilities in numerous groups, including individuals diagnosed with Alzheimer’s disease (Cahn-­Weiner, Ready, & Malloy, 2003), vascular dementia (Boyle, Paul, Moser, & Cohen, 2004), postacute brain injury (Farmer & Eakman, 1995), HIV infection (Heaton, Marcotte, et al., 2004), and schizophrenia (Jeste et al., 2003), as well as in community-­dwelling older adults (Bell-McGinty, Podell, Franzen, Baird, & Williams, 2002; Royall, Palmer, Chiodo,

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& Polk, 2005). However, the relationship between neuropsychological performance and IADLs varies according to the task and patient group. As examples, in an HIVinfected group, deficits in learning, abstraction/executive functioning, and attention/ working memory were significant predictors of IADL failures and objectively assessed functional impairments (Heaton, Marcotte, et al., 2004), whereas action fluency was most predictive of IADL dependence using a different test battery and cohort (Woods et al., 2006). In a study of patients with Alzheimer’s disease, the executive component of working memory was related to money management (Earnst et al., 2001), although disproportionate impairments of episodic memory are generally seen as most disabling in Alzheimer’s disease.

Vocational Functioning/Employment In patients of working age, perhaps the most common question posed to neuropsychologists is whether the patient will be able to return to work, and if so, what types of work will he or she be able to perform? Vocational outcomes have been defined via dichotomous, or multitiered, endpoints (e.g., fully employed/partially employed/ unemployed), or at a more granular level, as hours worked or a qualitative assessment of whether there has been a decline in efficiency. Neuropsychological impairment status has been a modest predictor of vocational functioning in clinical groups, and is perhaps better at predicting failure than success (Guilmette & Kastner, 1996). In a review of vocational functioning and IQ in cognitively normal individuals (i.e., assessing the premorbid or developmental aspect of neuropsychological functioning), Heaton and Pendleton (1981) concluded that IQ was related to job level, in that IQ scores were generally lower in unemployed individuals and higher in employed persons with more challenging positions. Using a meta-­analytic approach across various patient groups, Kalechstein, Newton, and van Gorp (2003) found that intellectual functioning, executive system functioning, verbal learning and memory, and episodic learning and memory were the strongest predictors of employed versus unemployed status. As expected, the relationship between neuropsychological predictors and vocational status varies by neuromedical condition. For example, memory and attention predicted employment in traumatic brain injury (TBI) (Brooks, McKinlay, Symington, Beattie, & Campsie, 1987), whereas verbal learning predicted return to work in an HIV cohort (van Gorp et al., 2007), and executive functioning, working memory, and speed of information processing related to vocational functioning in patients with schizophrenia (McGurk & Mueser, 2006). Return-to-work analyses can be complicated by non-­neuropsychological issues, such as the presence of litigation, the patient’s disability income, and motivation to return to work, as well as factors such as premorbid functioning, age, and the availability and quality of rehabilitation resources. Even with such complications, neuropsychological tests are still valuable predictive tools in examining vocational outcomes. For example, in a comprehensive study of TBI and vocational functioning, Machamer, Temkin, Fraser, Doctor, and Dikmen (2005) found that neuropsychological tests were useful in predicting postinjury work status, even after controlling for a variety of preinjury factors and injury severity. However, given the potential impact of these other factors, Guilmette and Kastner (1996) recommended that all assessments used to predict vocational

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functioning include evaluations of psychosocial/psychological functioning in order to improve predictive power.

Automobile Driving Driving is perhaps the most complex, and dangerous, everyday activity for many adults. Safe driving requires numerous abilities, including intact attention, perception, tracking, choice reactions, sequential movements, spatial judgment, and planning. Attempts to predict on-road driving behavior through the use of traditional neuropsychological tests have met with mixed success. Some studies have found neuropsychological performance to be associated with on-road abilities (Fitten et al., 1995; Hunt, Morris, Edwards, & Wilson, 1993; Odenheimer et al., 1994) and driving simulator performance (Marcotte et al., 1999; Rebok, Bylsma, Keyl, Brandt, & Folstein, 1995; Rizzo, Reinach, McGehee, & Dawson, 1997; Szlyk, Myers, Zhang, Wetzel, & Shapiro, 2002), whereas others have found poor relationships between driving abilities and cognitive assessments (Bieliauskas, Roper, Trobe, Green, & Lacy, 1998; Fox, Bowden, Bashford, & Smith, 1997). As with most studies addressing everyday functioning, attempts to summarize the field of driving research are complicated by the variety of populations sampled and methods used across studies. Researchers have used divergent test batteries and different gold standards regarding “driving impairment” (Molnar, Patel, Marshall, Man-Son-Hing, & Wilson, 2006; Reger et al., 2004; Withaar, Brouwer, & van Zomeren, 2000). For example, driving impairments have been determined via on-road drives, performance on driving simulators, and reviews of real-world crash or moving violation history. Using attention as one example of a cognitive ability associated with driving performance, lapses in attention have been cited in epidemiological studies as a key factor in accidents, perhaps occurring in 15–40% of all accidents (e.g., Stutts, Reinfurt, & Rodgman, 2001). In one project, the “100-Car Naturalistic Driving Study,” over the course of a year the investigators unobtrusively recorded data from 241 nonpatient drivers (a total of 2 million miles). There were 83 crashes and 761 near­crashes (i.e., requiring a rapid, severe evasive maneuver to avoid a crash). Driver inattention was cited as the cause in 78% of the crashes and 65% of the near-­crashes that occurred during the study (Klauer, Dingus, Neale, Sudweeks, & Ramsey, 2006). This study was done with healthy control participants. In patient groups, performance on measures of attentional processes such as divided attention have shown a strong relationship to driving performance (Brouwer, 2002; Lengenfelder, Schultheis, Al-­Shihabi, Mourant, & DeLuca, 2002; Uc et al., 2006b). The Useful Field of View (UFOV) test (Ball, Beard, Roenker, Miller, & Griggs, 1988; Sims, Owsley, Allman, Ball, & Smoot, 1998), a computerized measure that assesses both divided and selective attention by measuring the amount of time it takes an individual to accurately acquire both central and peripheral visual information without head or eye movements, may be a particularly sensitive indicator of driving impairment. UFOV declines with normal aging and is significantly reduced in many patient populations, including persons with TBI (Fisk, Novack, Mennemeier, & Roenker, 2002), multiple sclerosis (Schultheis, Garay, & DeLuca, 2001), stroke (Fisk, Owsley, & Mennemeier, 2002; Mazer et al., 2003), HIV (Marcotte et al., 2006), and mild Alzheimer’s disease (Duchek, Hunt, Ball, Buckles, & Morris, 1998). Reduced UFOV performance has

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been correlated with higher rates of past (Ball, Owsley, Sloane, Roenker, & Bruni, 1993) and future automobile crashes (Owsley et al., 1998) and associated with poor performance during on-road driving evaluations (Duchek et al., 1998; Myers, Ball, Kalina, Roth, & Goode, 2000). Attention, of course, is but one example of the cognitive domains that may need to be intact to drive safely. Depending on the patient group and outcomes, other cognitive domains implicated in driving performance include visuospatial functioning (Amick, D’Abreu, Moro-de-­Casillas, Chou, & Ott, 2007; Galski, Bruno, & Ehle, 1992; Grace et al., 2005; Hunt et al., 1993; Lundberg, Hakamies-­Blomqvist, Almkvist, & Johansson, 1998; Reger et al., 2004; Schanke & Sundet, 2000), executive functioning (Daigneault, Joly, & Frigon, 2002; Marcotte et al., 2004; van Zomeren, Brouwer, & Minderhoud, 1987; Whelihan, DiCarlo, & Paul, 2005), and processing speed (Stolwyk, Charlton, Triggs, Iansek, & Bradshaw, 2006; Uc et al., 2006b; Worringham, Wood, Kerr, & Silburn, 2006). However, there is currently no consensus regarding which neuropsychological measures best identify high-risk drivers. There is a general agreement, though, that cognitively impaired individuals as a group perform significantly worse than controls on driving measures, and the risk of a crash increases with higher levels of cognitive impairment (Withaar et al., 2000). Many factors beyond neuropsychological ability can affect driving performance, including motivation, personality, driving experience, use of medications and other substances with CNS effects, and road conditions. See Marcotte and Scott (2009) for a more detailed discussion of neuropsychology and the prediction of driving ability. As yet, there is no clear answer as to which neuropsychological tests are most predictive of the many components of real-world functioning, even, as noted above, when narrowing the question down to specific real-world tasks and neurological disorders. Whereas a number of studies has shown “modest” results in using specific neuropsychological tests to predict driving ability, for example, it is worth noting that in most cases these studies do not yield cutpoints that can guide the clinician in determining fitness to drive for an individual person. We can have the most confidence in the very general statement that global cognitive impairment is associated with worse performance on everyday functioning measures. Neuropsychologically, overall impairment levels can often be best estimated using summary scores such as the Average Impairment Rating from the Halstead–­ Reitan Battery, or a Global Deficit Score calculated from a reasonably comprehensive battery (Carey et al., 2004; Heaton, Miller, Taylor, & Grant, 2004). At the domain­specific level, a broad review of the literature suggests that executive measures may be the strongest and most consistent predictors of everyday functioning, in concurrence with the notion that complex measures better correlate with the complex aspects of real-world functioning (Chaytor & Schmitter-­Edgecombe, 2003; Goldstein, 1996; McCue, Rogers, & Goldstein, 1990). Thus, it has been argued that future research should specifically focus on executive functioning as a predictor of real-world performance (Cahn-­Weiner et al., 2003; Guilmette & Kastner, 1996). In addition, recent studies have implicated learning/memory in predicting real-world behavioral functioning (Heaton, Marcotte, et al., 2004; van Gorp et al., 2007). But these conclusions are by no means universally true, and the utility of specific measures, and even specific cognitive domains, still remains to be determined.

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Defining “Everyday Functioning” Outcomes One of the challenges in relating neuropsychological performance to real-world functioning is the lack of agreed-upon best methods for determining impairments in everyday abilities. Should we simply ask patients how they are doing in their daily lives? Should we require documentation of their daily performance, something that is often difficult to come by, if not entirely nonexistent? How about asking a third party who may only witness the patient performing tasks under specific circumstances? Or is it best to try to objectively measure the patient’s ability to carry out an everyday task, even though this test would be conducted in a controlled environment and perhaps have limited real-world applicability? As noted by Goldstein (1996), “tests or predictors and outcome measures or criteria are both surrogates for actual abilities and behaviors” (p. 84). There is a tendency in the literature to accept various outcome measures as being closely related to real-world functioning, but we must pay as much attention to the outcome, how it is measured, and its relationship to actual real-world tasks/functioning as to the predictors themselves. For example, is slowing on a task in which the individual is required to press a brake pedal when a stimulus on a computer screen changes color evidence of a reduction in “driving ability”? The use of outcome measures is addressed in various chapters in this volume; here we touch upon them briefly since they are critical in understanding the relationship, or lack thereof, between performance on neuropsychological measures and “real-world” outcomes.

Self-­Report Directly asking patients/participants how they are functioning in the world is the most relied-upon method for assessing real-world outcomes; in many cases it is the most practical and may give a reasonably accurate representation of real-world performance. This method is also advantageous because it provides important information regarding patients’ perception of their status, even if it lacks external validity in some cases. One example of a self-­report instrument is the Patient’s Assessment of Own Functioning Inventory (Chelune, Heaton, & Lehman, 1986), in which participants detail complaints regarding the frequency of everyday difficulties with memory, language, communication, use of hands, and higher-level cognitive and intellectual functions. However, self-­report measures often have a less clear relationship to formal testing than reports from informants or clinical ratings, particularly in neurological populations (Chaytor & Schmitter-­Edgecombe, 2003). Numerous studies have demonstrated that self-­report is susceptible to biases based on the individual’s mood and cognitive status. For example, depressed individuals tend to manifest negative self-­judgments across multiple domains and may underestimate their true abilities (see Moore et al., Chapter 17, in this volume, for a detailed discussion of depression and everyday functioning). In fact, Heaton, Chelune, and Lehman (1978) found that cognitive complaints were more closely related to results on the Minnesota Multiphasic Personality Inventory (MMPI) than to neuropsychological test results. On the other hand, individuals with impairments in metacognition and self-­awareness may

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be prone to underreporting their real-world deficits (Cahn-­Weiner et al., 2003; Patterson, Goldman, McKibbin, Hughs, & Jeste, 2001). Other factors such as litigation and the possibility of secondary gain may also influence self-­report.

Significant Others (Collateral/Proxy) Another common approach to assessing real-world outcomes is to ask for input from an informant, such as a spouse or caregiver. Such persons may be in a position to give the most accurate reports of how the patient handles everyday activities, but there are limitations to this approach. The informant may be biased, not know the patient well, or see the person only in situations in which his or her functioning is maximized (or minimized). Caregivers may be particularly influenced by certain obvious deficits; for example, ratings of memory functioning may be more influenced by word-­fi nding difficulties than by actual memory impairments (Cahn-­Weiner et al., 2003). There are indications that many informants tend to overestimate patients’ abilities (Loewenstein et al., 2001). And, of course, the patient and caregiver may disagree regarding each other’s assessments, perhaps making it difficult to determine which view is more accurate.

Ratings by Clinicians Clinician ratings are often used as an outcome measure. Examples include the Global Assessment of Functioning (GAF) and the Clinical Dementia Rating Scale. A key disadvantage to this approach is that clinicians have only what they see before them in the clinic—a snapshot of the person’s functional level. Moreover, clinicians are also subject to biases and often place significant emphasis on input from the patient and/ or caregiver (studies suggest that caregiver input carries the most influence). Some studies have found that the clinician’s judgment more closely matches performance on neuropsychological tests than the caregiver’s reports, possibly because the neuropsychological and clinical evaluations occur in the same structured environment. Although the approaches may lead to common conclusions, they still may not reflect real-world performance as closely as reports of an observer in the everyday living environment.

Manifest Functioning Another approach is to seek external documentation of real-world deficits, such as examining employment history, official driving records, or medical records (e.g., for medication adherence measurements). This approach better reflects how people function in their everyday lives and perhaps provides insights regarding whether, due to noncognitive factors, individuals perform better (e.g., using compensatory strategies) or worse (e.g., due to environmental limitations) than one would expect, based on their functional capacities as assessed in the laboratory. This approach, however, also can be prone to error. For example, employability can be influenced by factors other than capacity (mood disorders, environmental factors, reluctance to give up disability income support, etc.). And in the case of driving ability, crashes are rare, often only reported to authorities in more severe cases and may be related to many external fac-

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tors (e.g., other drivers, road conditions). Crash history can also be influenced by risk exposure (i.e., driving mileage, urban vs. rural driving, traffic conditions) and may thus not provide an accurate reflection of a person’s true driving ability. Actual everyday functioning can be assessed at the “molar” level (e.g., is the individual employed vs. unemployed?) (Goldstein, 1996) or at a more granular level (e.g., is the individual as effective at his or her job as in the past?). It appears that composite global cognitive test measures often best predict molar outcomes, perhaps because both types of variables encompass a broad range of abilities (Franzen & Wilhelm, 1996).

Direct Observation in the Real World Arguably, the most valid determination of “real-world” outcomes would be direct observation of the person in the real world. Ideally, this observation would occur unobtrusively, without the person’s awareness, since the act of being observed can change behavior. This approach is very difficult to implement, however, and likely to be costly and time-­consuming, although new technologies make it feasible to observe certain behaviors with increased subtlety and across extended periods of time. For example, in the 100-Car Naturalistic Driving Study of automobile driving (Klauer et al., 2006), healthy individuals agreed to have their own automobiles outfitted with equipment that recorded not only data (e.g., steering and braking), but also video of the driver and the view out the windows. Investigators could then witness each person’s behavior right before a crash or near-crash. Of course, such methods can potentially engender ethical concerns (e.g., what if the investigator witnesses illegal behavior of someone who has not consented to be observed?) as well as analysis challenges (e.g., how does the investigator conduct data reduction on 42,300 hours of multimodal data?), among other issues. But such advances do represent exciting new options for observing how patients with neurological conditions truly behave in the open environment.

Factors Complicating the Relationship between Neuropsychological Performance and Everyday Functioning As one considers everyday functioning, a distinction needs to be made between an individual’s capacity to do a task and the actual execution of that capacity. Goldstein (1996) refers to this distinction as the difference between ability—a skill or talent within the individual, which is assessable via neuropsychological testing—and function—the exercise of that ability in an environmental context. A person develops an impairment in ability (e.g., attention), which may then lead to functional deficits or disability (e.g., in driving an automobile). Clinic-based tests typically focus on capacity/ability, whereas in predicting real-world behavior, in addition to understanding what the person is capable of doing, we are also concerned with what the person actually does. In order to understand the limitations in using laboratory measures to predict real-world functioning, it is also important to remain cognizant that the person being evaluated must function within a changing environment and under varying contexts (Tupper & Cicerone, 1990), which can make success in the activity more or

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less likely. Unlike the laboratory testing situation, everyday functioning is not standardized across people and time. Below we briefly review some of the factors that might complicate the relationship between test performance and real-world functioning.

Testing Environment Neuropsychological assessment typically emphasizes the elicitation of “optimal performance” from an individual in order to determine the person’s underlying capacity (Lezak, Howieson, & Loring, 2004). By design, external factors (e.g., noise, distracting stimuli), task complexity (e.g., multitasking), and task length (many tests are relatively brief) are kept to a minimum. Even the newer ecologically oriented instruments (Rabin et al., 2007), which may encompass a variety of tasks, are often designed to be carried out within a clinic setting where distractions are minimized. In contrast, in the real world tasks are typically undertaken in environments where there are distractions, no direction, and limited encouragement.

Specificity of the Neuropsychological Test Neuropsychological tests are often cited as measures of specific cognitive constructs. Yet identification of these constructs can vary from author to author, adding to the difficulty in consistently identifying cognitive domains that are critical to real-world functioning. For example, the Trail Making Test Part B (Army Individual Test Battery, 1944; Reitan & Davidson, 1974) is often considered one of the measures most sensitive to brain dysfunction. In the literature it has been referred to as a measure of “complex visual scanning,” “speed of executive functioning,” “cognitive flexibility,” “visual–­perceptual processing speed,” and “set switching ability” (Gunstad et al., 2008; Kennedy, Clement, & Curtiss, 2003; Lezak et al., 2004; Schwab et al., 2008; Wobrock et al., 2007). Factor analyses have led investigators to consider it among measures of focused attention and perceptuomotor speed (Kelly, 2000; Mirsky, Anthony, Duncan, Ahearn, & Kellam, 1991), executive functioning (Heaton et al., 1995), or rapid alternation between mental sets (Tchanturia et al., 2004). The truth, of course, is that it has aspects of all of these constructs and receives a label of “X” due to the specific factor analysis that was conducted, the other measures included in the analyses, the subject group, or the author’s own interpretation of the measure.

Multiple Cognitive Determinants of Real-World Functioning As noted earlier in this chapter, most everyday tasks involve multiple cognitive processes, even tasks that may appear simple, such as making toast (Hart, Giovannetti, Montgomery, & Schwartz, 1998) or coffee (Giovannetti, Schwartz, & Buxbaum, 2007). Thus, determining the relationship between the cognitive ability and performance of a real-world task depends not only on how important the specific ability is to the task, but also the person’s degree of impairment in that ability. Some activities may have a threshold whereby significant impairment in a single domain, even if it is not considered critical to the task, can impact the ability to carry out the task. For example, attention and basic arithmetic skills may be key to managing a check-

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book, but severe visuospatial impairments may outweigh the relevance of the intact domains.

Limited Sampling of Behavior Neuropsychological testing provides only a brief snapshot of behavior (Chaytor & Schmitter-­Edgecombe, 2003), whereas real-world tasks can take place over a long time period. A client may be able to rally resources for a brief testing period but have difficulty when that time is extended, perhaps due problems with stamina and fatigue (Chaytor & Schmitter-­Edgecombe, 2003) or limited attentional capacity. A real-life example can be seen in studies of the effects of high altitude/oxygen depletion on cognition. Barcroft and colleagues (1923), as quoted in Gerard and colleagues (2000), reported on the difference in test performance and real-world performance during their time on the Peruvian mountain Cerro de Pasco (altitude of 4,330 meters): “When we were undergoing a test, our concentration could by an effort be maintained over the length of time taken for the test, but under ordinary circumstance it would lapse. It is, perhaps, characteristic that, whilst each individual mental test was done as rapidly at Cerro as at the sea-level, the performance of the series took nearly twice as long for its accomplishment. Time was wasted there in trivialities and ‘bungling,’ which would not take place at sea-level” (pp. 59–60). Such may also be the case when individuals with neurological disorders attempt to carry out a brief laboratory test versus a day’s worth of activities.

Environmental Factors and Resources The ability to carry out everyday functions can be significantly impacted by the environment. For example, being able to safely drive an automobile may differ depending on whether a person is alone in the car, using a cell phone, or transporting a group of middle schoolers. Environmental factors differ between individuals and for the individual from moment to moment: During the course of a commute an individual may drive on both a rural roadway and a congested city street, and weather-­related driving conditions may change. A person’s work environment may also determine if cognitive declines impact vocational functioning: Mild declines may be very evident in a highly demanding work environment, and less so when the responsibilities are not as challenging (Chaytor & Schmitter-­Edgecombe, 2003). Environmental factors can be beneficial as well as detrimental. The availability of resources and support systems, such as electronic reminders or individuals who can guide the person through specific tasks and provide moral/emotional support, may help a person to be more successful in the real world than suggested by a laboratory assessment of his or her functional capacity. Unfortunately, as important as it is to assess environmental demands for each person, few studies incorporate such evaluations in a standardized manner.

Psychiatric and Substance Use Disorders Many psychiatric conditions, such as schizophrenia (Green, Kern, & Heaton, 2004), bipolar disorder (Martinez-Aran et al., 2007), and major depression (Covinsky, Fortinsky, Palmer, Kresevic, & Landefeld, 1997; Rytsala et al., 2005), can affect a per-

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son’s ability to initiate and complete ADLs and impact the reliability of self-­reported functioning (Heaton, Marcotte, et al., 2004). Although medications for these conditions often improve functioning, they can potentially have negative effects as well (e.g., on automobile driving). Acute, and in some cases chronic, substance use can also affect key everyday activities such as employment, financial management, and driving ability (Hser, Huang, Chou, & Anglin, 2007; Johansson, Alho, Kiiskinen, & Poikolainen, 2007; Logan, 1996; Najavits & Lester, 2008; Semple, Patterson, & Grant, 2003), although the literature using objective measures of functional capacity in these groups is limited.

Experience/Functional Reserve It is generally accepted that certain individuals, typically those with higher IQs, educational level, or occupational attainment, may be able to suffer greater brain insults before such damage manifests itself clinically (Satz, 1993; Stern, 2003). It has been hypothesized that individuals may have a “cognitive reserve” based on innate levels or, alternatively, reserve is expanded by exposure to schooling and other stimulating activities. For most individuals, repeated exposure increases the “automaticity” with which tasks can be completed, enhances their expertise, and perhaps increases reserve. One example of an everyday activity modifying brain structure can be found in a study of London taxi drivers. In this project, the more time the participant spent as a driver, the larger the hippocampal volumes (Maguire et al., 2000), suggesting the possibility of increased reserve. One might hypothesize that the more experienced individuals could suffer more brain abnormality prior to reaching a point where they no longer work at a minimally competent level.

Individualized Approaches to Problem Solving Even neurologically normal individuals approach the same task differently (Chaytor & Schmitter-­Edgecombe, 2003). For example, some people may spend a great deal of time ineffectively “organizing” their to-do lists, whereas others may focus on completing the tasks that are in front of them. Others may routinely and effectively use shopping lists or map out a driving route ahead of time. These idiosyncratic approaches to everyday life complicate the prediction of real-world performance; in some cases, a well-­developed “list-­making” approach may help individuals should they suffer a decline in functional capacity in the future.

Motivation Clients may be motivated to do their best during testing but perhaps have less motivation in the real world, or vice versa. For example, they may be able to avoid undesirable tasks at home if they feign an inability to do the tasks. In the example of forensic cases, clients may see benefits in not performing their best during an evaluation in order to get increased compensation. Even in nonlitigation cases, clients may simply lack the motivation to try their best across a battery of neuropsychological or everyday functioning tests. These motivational issues argue for the use of skilled examiners, conversant with such problems, even when computerized measures are

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being administered. A number of instruments assesses “effort,” symptom validity, and malingering in the clinic/lab (Green, Allen, & Astner, 1996; Hiscock & Hiscock, 1989; Morgan & Sweet, 2008; Rey, 1964; Tombaugh, 1997).

Physical Impairments As noted earlier, physical impairments can affect both ADLs and IADLs and should be considered in many neuropsychological or functional evaluations. The impact of physical impairments is evident in many neurological conditions (e.g., stroke, traumatic brain injury, multiple sclerosis) and across many real-world tasks (e.g., driving and vocational functioning).

Education and Literacy Although it is clear that educational levels and neuropsychological test performance travel together on many tests, and IQ is closely linked to educational and ultimately job attainment, little attention has been paid to the direct relationship between education and the ability to carry out everyday tasks. At lower levels of education, in particular, literacy (numeracy, reading and writing) may be an issue. Many resource­limited countries have high illiteracy rates. Inadequate numeracy (“the ability to understand and use numbers in daily life’’) may adversely impact health outcomes and everyday functioning in tasks such as reading food labels, interpreting bus schedules, and refilling prescriptions (Rothman, Montori, Cherrington, & Pignone, 2008). However, education and literacy are not completely synonymous, as individuals learn many life skills (e.g., how to count money) without formal education.

Compensatory Strategies Because clinic/laboratory assessments are typically highly structured and assess only a limited number of abilities, these evaluations may at times underestimate an individual’s capacity to perform in the open environment by not providing opportunities to implement compensatory strategies (Franzen & Wilhelm, 1996). Individuals may have learned strategies such as monitoring tasks using a to-do list or setting an alarm as a medication reminder. Thus they may function adequately in their daily life but do poorly in the clinic/laboratory when asked to learn a list of items or do a task at a certain time. On the other hand, they may make a concerted effort to strategize during a testing session, but not do so in everyday life. For example, a person might use semantic clustering to remember items on a memory test, but not use such a strategy when trying to remember a shopping list (Chaytor & Schmitter-­Edgecombe, 2003). In addition to providing information regarding an individual’s deficits, neuropsychological testing can provide valuable information regarding a person’s cognitive strengths, which may also suggest ways that he or she could potentially compensate for deficits. This is one reason why neuropsychologists should always consider assessing multiple domains, and not just those in which they hypothesize likely impairment (Heaton & Marcotte, 2000). Given the many factors that might affect everyday functioning, it is not surprising that, as with most behavioral research, measures of cognitive status alone remain

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only “moderately” related to real-world performance. Improving assessment in the areas mentioned above might enhance laboratory predictions of behavior in the open environment.

Selection of Neuropsychological Test Variables Is a common, underlying set of cognitive abilities necessary in order to adequately perform all everyday activities? Alternatively, is it the case that some key abilities (e.g., attention) are necessary, but perhaps not sufficient, to carry out many tasks, and specific activities require specific skill sets? Can we predict human behavior by examining performance on cognitive constructs individually and in isolation, or do we need to know how they work in concert? Although these questions remain unanswered, there appears to be general agreement among practicing neuropsychologists regarding the key abilities that should be assessed when predicting everyday functioning. These include attention, executive functions, intelligence, language, motor skills, verbal and nonverbal (visual) memory, construction, and visuospatial skills. Nevertheless, there remains significant variability with respect to which tests are used to assess these domains (Rabin et al., 2007). When predicting everyday functioning, most neuropsychologists use traditional tests and then may augment their battery with one or more ecologically oriented measures (see below). Neuropsychological tests can yield a number of performance variables: raw scores, scaled scores, and demographically adjusted scores. In order to determine whether there has been a decline in functioning, the examiner needs to know the patient’s premorbid functional level. However, neuropsychological testing is rarely available for the period prior to an insult (e.g., head injury). A variety of methods has been developed to estimate prior functioning, including measures based on educational and occupational attainment, as well as performance on tests that are relatively insensitive to acquired brain abnormalities (e.g., Barona & Chastain, 1986; Gladsjo, Heaton, Palmer, Taylor, & Jeste, 1999). Neuropsychological performance tends to travel with characteristics such as age, education, gender, and ethnicity (Heaton, Taylor, & Manly, 2004), and the use of norms that adjust for these factors are particularly helpful in estimating differences between observed and expected levels of performance. Although the method of using demographically adjusted normative standards works well for determining whether individuals are impaired relative to expected levels, it may be that the use of adjusted scores is not the best method for predicting performance of activities that most of the population can accomplish routinely. For example, although we might expect a person with a PhD in engineering to perform better on cognitive tests than an individual with a high school education, we would not necessarily expect that person to be a better driver or more adept at managing his or her medications. When addressing the relationship between cognition and everyday functioning, we are not so much concerned with whether someone has declined from a previous level of neurocognitive performance, but rather whether his or her functioning is adequate for everyday functioning requirements now. One approach to predicting competence in everyday skills would be to simply consider raw scores, such as time to complete the Trail Making Test Part B, or the learning rate on the California Verbal

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Learning Test. However, raw scores are difficult to compare across tests and to interpret in relation to expected functioning of the general population. For example, one measure may be timed, in which a fewer number of seconds indicates good performance, whereas higher scores on another measure (e.g., a list-­learning test) are indicative of good performance. These differences also make it difficult to combine such variables into summary scores. For these reasons we have recommended the use of scaled scores in predicting everyday functioning (Heaton & Marcotte, 2000). Scaled scores are uncorrected (e.g., for age) scores that are generated from a population of normal controls (ideally representing a broad range of demographic characteristics, similar to the society of interest; e.g., based on a national census), and transformed so that they are normally distributed (often with a mean of 10 and a SD of 3). Since each test variable is put onto this common metric, one can then compare performance across measures and generate summary scores, such as estimates of overall or domain-­specific functioning (Heaton, Miller, et al., 2004). There remains a fair amount of variability in whether investigators use raw, scaled, or T-scores. A recent study directly compared the use of adjusted and unadjusted scores (Silverberg & Millis, 2009) in a group of patients with TBI. Real-world outcomes were based on patient and caregiver reports. The authors used the normative data provided by Heaton, Miller, and colleagues (2004) to generate “absolute scores”—unadjusted scores that were placed upon the T-scale metric, where the overall mean of the normative group is 50, with a standard deviation of 10, in order to facilitate comparisons of the two methods. They then created two overall test battery mean scores (for absolute and adjusted scores) in order to predict outcomes on their questionnaires. The authors found that (1) absolute and adjusted scores were often divergent, usually based, as would be expected, on the degree to which the patient differed from the normative group average on demographic factors (age, education, gender, ethnicity); and (2) whereas both measures predicted everyday functioning, the results tended to favor the use of absolute scores. It should be noted, however, that the superiority of absolute scores for predicting everyday functioning may depend on whether the tasks are those that all or most adults would be expected to perform successfully. If the everyday tasks are exceptionally demanding and normally performed only by people with high levels of education (e.g., physicians, attorneys, scientists, university professors), use of education-­corrected scores may be better predictive of success or failure. Additional studies comparing these methods may yield useful insights as to the best way to use neuropsychological test results to predict real-world behavior. For example, such studies might identify absolute levels of functioning in various domains that are needed to accomplish specific tasks, such as medication management. The findings might vary by neurological group, but over time investigators could build a common base of knowledge that would inform clinicians and future studies.

Instruments Focusing on Ecological Validity There has been an increasing interest in developing test instruments and batteries that emphasize ecological validity. These instruments more closely resemble the activities individuals would be expected to undertake in everyday life, with the hope

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of better predicting real-world functioning. Many of these “performance-based” measures are designed to assess functional capacity—that is, the person’s ability to perform tasks under optimal circumstances. The focus is not on differentiating normal and patient groups, per se, but on “identifying people who have difficulty performing real-world tasks, regardless of the etiology of the problem” (Chaytor & Schmitter-­E dgecombe, 2003, p. 182). Thus, in theory, these tests should be applicable to many different patient groups and, in some cases, people within normal populations. Such tests are often well accepted by patients/participants, given their strong face validity. These measures typically have multiple subtests; examples of the types of tasks utilized include remembering names associated with faces, recalling a hidden object and its location, recalling an appointment when a timer sounds (Rivermead Behavioral Memory Test; Wilson et al., 1985); searching maps, looking through telephone directories, and listening to broadcasts of lottery numbers (Test of Everyday Attention; Robertson et al., 1996); clock reading, preparing a letter for mailing, eating skills (Direct Assessment of Functional Status [DAFS]; Loewenstein et al., 1989); role-­playing household chores (cooking, shopping), route-­planning for public transportation, and planning recreational activities (UCSD Performance-Based Skills Assessment [UPSA]; Patterson et al., 2001); managing medications, cooking, and performing financial management and vocationally oriented tasks (Heaton, Marcotte, et al., 2004). In a survey of almost 750 members of the major neuropsychological societies (International Neuropsychological Society, National Academy of Neuropsychology, American Psychological Society Division 40 [Neuropsychology]), Rabin and colleagues (2007) found no clear consensus regarding which ecologically oriented measures were most useful for predicting everyday functioning. The most common standardized measure was used by less than 10% of the respondents. In decreasing order, the following methods for assessing the domains of memory, attention, executive functioning, and determination of ability to return to work were the most frequently mentioned: •• Memory: Rivermead Behavioral Memory Test (Wilson et al., 1985), Autobiographical Memory Interview (Kopelman, Wilson, & Baddeley, 1990), Contextual Memory Test (Toglia, 1993), Brief Test of Attention (Schretlen, 2005), prospective memory tests. •• Attention: Test of Everyday Attention (Robertson et al., 1996), Behavioral Inattention Test (Wilson, Cockburn, & Halligan, 1987), observe patient’s compensatory strategies, clinical interview with functional questions, review work record and past job responsibilities. •• Executive functioning: Tinkertoy Test (Lezak, 1982), Behavioral Assessment of Dysexecutive Syndrome (Wilson et al., 1996), Behavior Rating Inventory of Executive Function (Gioia, Isquith, Guy, & Kenworthy, 2000), Six Elements Test (Shallice & Burgess, 1991), Dysexecutive Questionnaire (Wilson et al., 1996). •• Return to work: Driving evaluation, functional assessment, structured work trial, clinical assessment of current job demands, expectations, requirements; interview with coworkers/supervisors.

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As can be seen, there were many cases in which neuropsychologists emphasized clinical acumen and nonstandardized evaluations rather than published tests. Of those who used the ecologically oriented instruments (35% of the sample), 65% used only one measure, and 95% used three or fewer measures, even though 70% of all respondents reported encountering at least one rehabilitation-­related assessment referral question. As the authors note, this research “highlights the disparity between the proportion of neuropsychologists who conduct assessments that focus on ecological issues and the proportion who use the instruments designed for ecological purposes” (p. 736). If these instruments hold promise, why have neuropsychologists hesitated to incorporate such measures into their standard test batteries? Spooner and Pachana (2006) propose the following possibilities: (1) the assumption that traditional tests are ecologically valid, despite limited evidence that this is the case; (2) the tendency to stay with those instruments on which one received graduate training, or to remain committed to a particular theory of assessment approach; (3) the view that verisimilitude is synonymous with face validity, suggesting a less rigorous or “unscientific” evaluation of the ecological validity of the measure, even though many of these instruments have undergone such evaluations; (4) the belief that tests based on verisimilitude overlap with the occupational therapy approach and thus encroach on another discipline; (5) the belief that traditional tests measure specific constructs, even though “the application of labels to cognitive domains is not necessarily reflective of unambiguous empirical findings” (p. 334). Although many such instruments hold promise, mimicking everyday tasks in the clinic/lab does not necessarily mean that the findings will directly relate to how patients/participants function in the real world, where they must deal with competing tasks, prioritizing, paying attention in the context of distractions, and so on. As such, some investigators have tried to assess the sequencing and multitasking aspects of daily life. Burgess and colleagues (2006) advocate a “function-led approach” to creating clinical tasks—­models that proceed from a directly observable everyday behavior backward to examine how a sequence of actions leads to behavior, and how that behavior might become disrupted. Ecological validity may be improved because of more specific delineation of cognitive processes, even in seemingly simple behaviors (e.g., making toast and coffee) (Schwartz, 2006). As an example, although various executive functions have been proposed to be cognitive skills that are integral to the successful execution of everyday activities, “executive functions” can refer to a range of cognitive processes (e.g., planning, conceptualization, set shifting, attention, monitoring), any of which, if impaired, could lead to problems with everyday functioning. Isolating these processes is difficult because neuropsychological tests used to assess executive functioning (e.g., Trail Making Test Part B, Wisconsin Card Sorting Test, category test, Stroop tests, Tower of London test, fluency tests) generally assess more than one of these processes. Thus, taking a “function-led” approach, researchers have examined everyday behaviors that are problematic for patients with executive dysfunction by developing tasks informed by cognitive models of these behaviors. Many patients with dementia and frontal lobe disorders display problems with organization and execution of “everyday action”

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(Schwartz & Buxbaum, 1997), such as errors in carrying out a sequence of actions (e.g., preparing a lunch). One hypothesis is that the actual generation and sequencing of actions might be deficient in such a scenario. This hypothesis has been explored through tasks of script generation, in which individuals are asked to generate and properly sequence all the steps needed to complete an action (e.g., doing the laundry). Script generation deficits have been found in individuals with frontal lobe lesions (Sirigu et al., 1995), Parkinson’s disease (Godbout & Doyon, 2000), attention-­deficit/ hyperactivity disorder (Braun et al., 2004), and schizophrenia (Chan, Chiu, Lam, Pang, & Chow, 1999), and these deficits have been related to problems in everyday functioning (Chevignard et al., 2000). Assessing the generation and organization of such action sequences may be important in certain patient groups. On the other hand, the actual execution of an action sequence may result in errors in carrying out the task due to errors of omission (e.g., leaving out a critical step), commission (e.g., adding unnecessary steps), or substitution (e.g., using an object not relevant to the task). This possibility has been investigated with standardized laboratory tests that mimic everyday, multistep tasks, such as the Naturalistic Action Test (Schwartz, Buxbaum, Ferraro, Veramonti, & Segal, 2003), in which patients are asked to make toast, pack a lunchbox, and so on. On this and similar tasks, individuals with closed head injuries (Schwartz et al., 1998), stroke (Schwartz et al., 1999), schizophrenia (Kessler, Giovannetti, & MacMullen, 2007), and progressive dementia (Giovannetti, Libon, Buxbaum, & Schwartz, 2002) have a high number of errors relative to normal comparison participants. In addition, different patterns of performance on such tasks might distinguish clinical groups (Giovannetti, Schmidt, Gallo, Sestito, & Libon, 2006; Kessler et al., 2007), emphasizing the possible clinical utility of such an approach. Another possibility is that the coordination of various action sequences (i.e., switching between sequences) creates deficits in everyday action. This roughly corresponds with the construct of “multitasking,” in which an individual is required not only to plan and organize based on temporal and conditional associations between actions, but also to maintain this conditional and temporal information in working memory, along with other information such as the immediate environmental stimuli, goals, and subgoals. Given that this process was disrupted in a number of their patients with neurological conditions who displayed adequate performance on traditional executive function tests, Shallice and Burgess developed the Six Elements Test (1991) and Multiple Errands Test (Shallice, 1991) to provide patients with similar task demands as everyday life situations and thereby assess multitasking abilities. In each of these tests, individuals are asked to complete a number of simple tasks without breaking a series of prescribed rules. Subsets of patients with frontal lobe lesions (Burgess, 2000), depression (Channon & Green, 1999), schizophrenia (Evans, Chua, McKenna, & Wilson, 1997), Parkinson’s disease (Kamei et al., 2008), or multiple sclerosis (Roca et al., 2008) have shown profound deficits on such tests, with concomitant problems in real-world functioning. Importantly, despite their open-ended and naturalistic nature, such measures have generally displayed adequate psychometric properties (Knight, Alderman, & Burgess, 2002; Schwartz et al., 2002) and have shown moderately high correlations with independent outcomes assessing everyday functioning (Burgess, Alderman, Evans, Emslie, & Wilson, 1998). As such, they may offer ecologically relevant

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additions to a battery of assessment instruments when such everyday problems are suspected. Prospective memory, or the ability to execute a future intention (i.e., “remembering to remember”) in the absence of explicit cues, is another area garnering interest among investigators interested in predicting real-world performance. Examples include remembering to take a medication after a meal or mail a letter on the way home from work. Prospective memory is conceptually dissociable from retrospective memory, which refers to remembering information from the past in response to overt prompts. Initial studies attest to the ecological relevance of prospective memory, suggesting that it may even be a stronger contributor to the independent performance of IADLs than retrospective memory (Park & Kidder, 1996). Prospective memory is theorized to be dependent on the integrity of frontostriatal circuits (Simons, Scholvinck, Gilbert, Frith, & Burgess, 2006) and has been shown to be reduced in a number of conditions that affect these systems, including aging (Einstein, Holland, McDaniel, & Guynn, 1992; Maylor, Smith, Della Sala, & Logie, 2002), HIV infection (Carey, Woods, Rippeth, Heaton, & Grant, 2006), Parkinson’s disease (Katai, Maruyama, Hashimoto, & Ikeda, 2003), and schizophrenia (Kondel, 2002; Woods, Twamley, Dawson, Narvaez, & Jeste, 2007). Furthermore, prospective memory has been predictive of everyday functioning in individuals with schizophrenia (Twamley et al., 2008) and HIV infection (Martin et al., 2007; Woods, Carey, et al., 2007; Woods et al., 2008). Prospective memory may therefore be a unique and ecologically important aspect of cognitive functioning that, although ubiquitous in daily life, is not captured by traditional assessment techniques. In some cases, individuals display a number of errors on such assessment instruments while performing adequately on more traditional measures of similar constructs. However, many of the instruments that have been developed using a functionled approach are still being used predominantly in clinical research and await further validation and normative standards before being widely used in clinical care.

Challenges in Developing Ecologically Oriented Measures Ideally, it would be useful to employ ecologically oriented measures that encompass a broad range of skill levels (easy to challenging) and are able to detect subtle declines (in the case of early-stage neurological disorders) or improvements (in the case of pharmaceutical treatments). However, it is very difficult to develop measures that reflect everyday functioning—tasks that most people successfully perform in their daily lives—and are still challenging enough to provide a distribution of functioning across “normal” individuals (i.e., so that not everyone either receives a perfect score or fails the test). As the difficulty of a task increases, it becomes a challenge to keep the measure from being “test-like” (Goldstein, 1996) or game-like. For example, how much complexity can be added to a money management task before the testee would need to be a certified public accountant to succeed on the test, or at what point does adding difficulty to a driving simulation (e.g., accident avoidance scenarios) produce the look and feel of an arcade videogame, thus losing the real-world aspects of the measures? The Rivermead Behavioral Memory Test is an example of a measure that was “extended” when the earlier version was found to be insufficiently challenging

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to delineate functioning within normal individuals (de Wall, Wilson, & Baddeley, 1994). From our own experience, our battery of functional measures (cooking, shopping, financial management, medication management, vocational abilities) underwent a number of modifications before achieving a reasonable balance between task difficulty and real-world applicability (Heaton, Marcotte, et al., 2004). In addition, given that most healthy individuals perform near ceiling on many everyday measures, it is also often challenging to establish test–­retest reliability via traditional correlational methods.

Using Both Traditional and Ecologically Oriented Instruments to Predict Everyday Functioning Measures specifically designed to assess abilities related to an everyday task, but not directly mirroring the task of interest, may provide incremental improvement on the prediction of everyday functioning achieved using traditional neuropsychological measures. For example, in a study of automobile driving with HIV infection, poor neuropsychological functioning was associated with recent on-road crashes (Marcotte et al., 2006). However, by further stratifying participants according to performance on the UFOV test (Ball et al., 1993; Ball & Roenker, 1998), a computerized measure of visual divided attention, we were able to identify those participants who had the highest number of crashes. It is thus possible that individuals with impaired visual attention and concomitant executive deficits are less likely to be aware of their attentional deficits and thus fail to take steps to compensate for their impairments (e.g., driving more slowly). In addition, measures that more closely reflect real-world tasks may also add incremental information useful in predicting real-world performance. For example, in a different study of driving abilities of HIV-positive individuals (Marcotte et al., 2004), we examined performance on a structured, on-road driving assessment. Participants completed a detailed neuropsychological test battery and interactive PCbased driving simulations assessing routine driving and accident avoidance skills, as well as navigational abilities (i.e., using a map, participants were asked to drive to a location within a virtual city and then return to their starting location). Global neuropsychological performance was a significant predictor of passing or failing the onroad drive. However, performance on the simulations explained additional variance beyond traditional testing in predicting on-road performance, suggesting that the simulations may provide information on real-world behaviors that are not captured by the neuropsychological measures, such as the ability to anticipate high-risk situations or respond to complex demands when under time pressure.

What Is the Best Lab-/Clinic-Based Approach to Predicting Real-World Behavior? As noted earlier, the existing literature suggests a “moderate” relationship between traditional neuropsychological measures and real-world functioning, and no single test, or battery of tests, is predictive of all aspects of everyday functioning across all

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groups. However, the neuropsychological approach brings many advantages, in that many tests have good psychometric properties, established reliability and validity, and norms; in addition, there is abundant evidence that performance on traditional neuropsychological tests relates to aspects of everyday functioning. Few studies have done direct comparisons between approaches emphasizing veridicality versus verisimilitude, and comparisons between studies are complicated by the use of different test instruments, different outcome measures, and different samples. However, in a review of studies using one, or both, approaches, Chaytor and Schmitter-­Edgecombe (2003) found some evidence favoring the verisimilitude approach in predicting everyday performance, at least with respect to memory and executive functioning. But the matter is still unresolved. At this juncture, it appears that the best approach remains one in which, in most circumstances, the neuropsychologist uses demographically adjusted scores to determine whether there has likely been a decline from previous cognitive levels. If the decline appears to be of sufficient magnitude to affect everyday functioning, the examination of nonadjusted scaled or absolute scores can be used to predict most real-world activities (Silverberg & Millis, 2009). Greater precision of this prediction is likely to be possible if future studies help clarify basic levels needed to perform specific tasks. In some cases of particularly highly demanding positions (e.g., physician, pilot), it is advisable to continue to focus on expected levels of cognitive functioning, using demographic corrections, since an average level of scaled scores may not adequately encompass the cognitive expertise needed for the most challenging real-world tasks. Based on a broad review of the literature, a focus on executive functioning (Guilmette & Kastner, 1996) and perhaps learning and memory (Chaytor & Schmitter-­Edgecombe, 2003; Heaton, Marcotte, et al., 2004) may provide the greatest yield regarding the prediction of real-world functioning. Additional cognitive domains specific to the real-world tasks in question could also be assessed. As noted earlier, since one is also interested in cognitive strengths (e.g., for potential compensatory mechanisms), we recommend the administration of a comprehensive battery whenever the prediction of real-world functioning is the goal. It should also be clear that there are benefits to the multimodal assessment of an individual’s ability to carry out everyday tasks successfully. Such assessments would include information gleaned from some of the well-­developed, ecologically oriented measures discussed throughout this chapter, as well as perceptions regarding how well the individual is functioning in his or her daily life (based on self-­reports and knowledgeable informants). Traditional neuropsychological tests and performancebased everyday functioning measures inform us of the individual’s capacity, but the clinician also needs to be familiar with other factors (e.g., environmental, emotional, psychosocial) that might cause differences between capacity and implementation.

Future Directions Traditional neuropsychological measures continue to prove useful, as just noted. However, although there will always be a need for measures that assess specific cognitive constructs (e.g., for diagnosis), the field is still faced with the question of how to

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develop new tests or more effectively use the old ones to better predict functioning in the real world. We are seeing a merging of traditional approaches and ones based on verisimilitude, and it appears that such methods can be used in a complementary way to predict real-world functioning and also be standardized with good psychometric properties. Yet some have argued for an even more significant paradigm shift in how we go about identifying and measuring the cognitive abilities involved in carrying out everyday tasks. For example, as noted earlier, Burgess and colleagues (2006) make a case for going from “function to construct,” rather than adapting tests developed from experimental investigations. Using this function-led approach in developing their Multiple Errands Test, they progressed from designing a measure of “real-world” behavior that they believed captured the behavioral disorganization seen in patients with neurological conditions, to a series of studies ultimately examining the neural underpinnings of subfunctions, ending with an hypothesized construct of an attentional “gateway” (Burgess et al., 2006). In a similar vein, Kingstone, Smilek, and Eastwood (2008) contend that the usual approach of emphasizing invariance and control in cognitive research, using constrained laboratory paradigms, is “incompatible with the ecological goal.” Stating that general systems theory “has demonstrated that tight experimental control can be effective at revealing the basic characteristics of simple linear systems but it is ineffective at revealing the characteristics of complex, non-­linear systems, which must surely include the human cognitive system,” these authors emphasize observing and describing behavior as it occurs in the real world, rather than expecting models developed in the lab to pertain to the open environment (p. 320). They also encourage the elicitation of input from participants themselves, in order to garner their subjective and introspective experience and better understand individual approaches to a task. This approach, termed “cognitive ethology,” is described as more compatible with basic research and discovery. It may not have immediate application with respect to predicting real-world performance from an office setting, but it may yield new insights that inform the development of implementable, and standardized, measures. Consistent prediction of real-world behavior is also complicated by the fact that, for the ecologically oriented instruments that have been developed, most have yet to gain widespread use, either by different research groups or across different neurological populations. Many tools are “home grown” and applied within a single laboratory or across only a few patient groups, thus limiting their utility to the field at large. Until these approaches are more widely implemented, it is likely that the field will progress slowly. Since such movement is unlikely to occur when initiated by only a handful of investigators sharing a common interest, and funding for such development is scarce, it is recommended that neuropsychological organizations or government entities invested in understanding outcomes (e.g., the National Institutes of Health) foster such collaborations, perhaps assembling expert panels to weigh in on the design of new instruments, as well as methods of implementing them across multiple sites. This recommendation is certainly not meant to inhibit the critical role played by the individual investigator creatively developing novel measures. But to truly advance the field to a point where such instruments can become the standard of care, there needs to be widespread acceptance and application of these measures.

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Another area warranting both increased individual initiative and a fostering of collaboration is the application of ecologically oriented measures as outcomes in clinical trials. Over the past decade greater importance has been placed on the question of whether behavioral or pharmaceutical interventions significantly improve real-world functioning and quality of life. Further development of neuropsychological norms for change—an approach that will help clinicians determine whether improvements or declines are “unusual”—will only enhance the utility of the neuropsychological approach (Heaton & Marcotte, 2000). However, these neuropsychological measures do not necessarily meet the spirit of the requirement to assess functional outcomes (e.g., is significant improvement in Trails B time important if it fails to translate into a functional change in daily life?). For this reason, there have been calls for better measurement of outcomes relating to everyday functioning. For example, the U.S. Food and Drug Administration (FDA) requires that clinical trials for the treatment of Alzheimer’s disease (Laughren, 2001) and schizophrenia (Buchanan et al., 2005) include co-­primary measures that assess a clinically meaningful/relevant functional outcome. There thus may be greater movement toward measures that include a verisimilitude approach to predicting real-world behavior, if indeed such measures are better predictors. As noted here, and throughout this book, one needs to pay as much attention to the measurement of outcomes as to predictors. This lack of attention to outcomes is still a limitation for many studies examining real-world functioning, since the “real-world” outcome is itself poorly defined. In the field of automobile driving, for example, the relationship between cognitive performance and “driving” may differ if driving performance is assessed via reaction time to a video, a fully interactive desktop simulator, a full-­motion car cab, a closed-­course challenge drive, an open-road assessment, or a tally of real-world crashes. New technologies hold promise for unobtrusively observing some important behaviors in the open, real-world environment. As described earlier, in the “100-Car Naturalistic Driving Study” the investigators outfitted the research participants’ own cars with equipment to measure, and video, individuals’ behavior on the real road over the course of a year (Klauer et al., 2006). Other techniques have been used to monitor natural movement of impaired individuals in their homes (Hayes et al., 2008). Although such approaches can raise ethical issues and present data informatics challenges, they offer an exciting preview of a coming capacity to observe how normal, and impaired, individuals behave in their day-to-day life. The ability of neuropsychological testing to predict everyday functioning has been clearly established. However, performance on these clinic-based measures does not capture all of the variance associated with behavior in the open environment. Advances in theoretical conceptualizations, test development, technology, and multimodal methods of assessing predictors and outcomes portend a promising future for our ability to understand the relationship between brain function and behavior in the real world.

Acknowledgment We wish to thank Rachel Meyer for her assistance in the preparation of this chapter.

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Chapter 2

Understanding the Relevance of Human Factors/ Ergonomics to Neuropsychology Practice and the Assessment of Everyday Functioning Wendy A. Rogers, Andrew K. Mayer, and Cara B. Fausset

U

nderstanding human–­system interactions is the broad goal of the field of human factors/ergonomics (HF/E). The characteristics of the human that are relevant to such interactions include physical as well as cognitive capabilities. The “system” can range from something as simple as a can opener to something as complex as the cockpit of a jet airplane or the control room of a nuclear power plant. This chapter focuses on the cognitive capabilities of humans that influence their interactions with systems encountered in the context of everyday activities, such as computers, medical devices, medications, and transportation systems. HF/E practitioners investigate the capabilities and limitations of people and the demands placed upon them when performing activities from the most basic everyday tasks to the most complex vocational tasks. Our goal here is to illustrate the relevance of the knowledge base and the tools of the HF/E field to issues faced by neuropsychologists and their patients. For example, neuropsychologists and occupational therapists could use HF/E tools and techniques to understand more completely the cognitive and perceptual functioning of a patient with traumatic brain injury; this knowledge could then guide interventions to facilitate everyday functioning for that individual. These tools can also be used to develop strategies that help individuals with general memory deficits to perform demanding tasks such as managing a medication regimen. One of the major benefits of the HF/E tools and techniques discussed herein is their potential applicability to a wide range of people and systems. The chapter is organized as follows. We first provide an overview of the HF/E field. Next, we describe the tools and techniques used in HF/E to understand human–­ system interactions, identify problems, and develop solutions (i.e., asking the right questions and answering them). We then provide illustrative examples of these tools



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and techniques as they have been applied in various domains. The domains we selected mirror the activities of everyday functioning that are addressed in the other chapters of this volume. We conclude with a brief discussion of some future directions in HF/E, including neuroergonomics and adaptive automation.

Defining the Discipline of Human Factors/Ergonomics HF/E is a “unique and independent discipline that focuses on the nature of human–­ artifact interactions, viewed from the unified perspective of the science, engineering, design, technology, and management of human-­compatible systems, including a variety of natural and artificial products, processes, and living environments” (Karwowski, 2006, p. 4). In the United States a distinction is often made between “human factors,” referring to perceptual and cognitive characteristics of people and the systems with which they are interacting, and “physical ergonomics,” referring to anthropometry and biomechanics. In other nations the broad term “ergonomics” is used to refer to the whole discipline. In this chapter we use the abbreviation HF/E. HF/E practitioners are generally interested in three goals: to enhance system performance, increase safety, and increase user satisfaction (Wickens, Lee, Liu, & Becker, 2004). These goals are generally achieved by analyzing and understanding the cognitive and physical capabilities and limitations of the user as well as the physical and information systems with which he or she interacts through the use of appropriate analytic tools. Adding HF/E tools to neuropsychologists’ and occupational therapists’ toolkits provides them with the means to better understand neurological populations and the systems with which they interact. The breadth of the field is illustrated by the range of technical specialties within it. The Human Factors and Ergonomics Society (HFES) was founded in 1957 and currently has approximately 5,000 members. HFES has technical groups to support the exchange of knowledge within specialty areas; these technical groups are listed in Table 2.1. This list demonstrates the range of HF/E applications (e.g., aging, communication, health care, the Internet, transportation) as well as the varied research methodologies used in the field (e.g., cognitive engineering, human performance modeling, test and evaluation).

TABLE 2.1.  Technical Groups of the Human Factors and Ergonomics Society Aerospace systems Aging Cognitive Engineering and Decision Making Communications Computer Systems Education Environmental Design Forensics Professional Human Performance Modeling Health Care Systems Individual Differences in Performance Note. Data from www.hfes.org.

Industrial ergonomics Internet Macroergonomics Perception and Performance Product Design Safety Surface Transportation System Development Test and Evaluation Training Virtual Environments

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Asking the Right Questions—and Answering Them HF/E is a diverse discipline that ranges from transportation to health care and from nuclear power plants to the football field. How can the tools and techniques used by HF/E practitioners span such a seemingly broad chasm of domains? Simple: by asking the right questions in the right situations. The purpose of asking the right questions is to identify user–­system problems, to pinpoint the source(s) of the problems, and to understand and identify potential solutions. Although there is no formula to guide the question-­asking process, there are commonly asked questions that may serve as a starting point (see Table 2.2). Given the HF/E focus on the person, the system, and the interaction between them, the relevant questions encompass these variables. The basic tenet of HF/E is to “know thy user.” The corollaries are to understand the system and the context of use. The first step is to understand the person. What are the capabilities and limitations in terms of the physical, perceptual, and cognitive characteristics of the people who are interacting with the system? This question can be answered through observation, interviews, and surveys, as well as through an understanding of the typical capabilities and limitations for the user group (e.g., children, older adults, those with visual impairments). The person analysis must be specific. For example, we would not assess engineers to ascertain the problems individuals with cognitive impairments would have navigating through an environment or interacting with a system; we would assess individuals with such impairments. This point may seem obvious, but unfortunately decisions are often based on the beliefs of designers, rather than on specific user capabilities and limitations. The system characteristics must also be analyzed. What are the physical, perceptual, and cognitive demands imposed by the system itself? Does it require fine motor control, processing of multiple sources of information, or the comprehension of complex instructions? Is monitoring of automated components required? System analysis can be accomplished through task analysis and process diagrams (described later). Understanding the characteristics of the system is essential to link users’ capabilities and limitations to the system’s demands and requirements. The third set of questions relates to understanding more about the interaction between the person and the system. What is the context of use (e.g., time pressure)? What type of instruction and feedback is provided during the interaction? Is the situation static or dynamically changing? Consider an analysis of a person using an in-­ vehicle navigation system. It is important to know if the user is an experienced driver, has familiarity with the system, can process both visual and auditory information, has constraints on attention or working memory, and so on. Details of the system must also be understood, such as the type of input device that is used to interact with the system, the amount of information that is displayed, and the format in which it is displayed, among other factors. It is also relevant to understand the context of the interaction; for example, if the system will be used while the person is driving, the display can be viewed only for limited amounts of time (while taking eyes off the road), input can be made with only one hand, and decisions may have to be made quickly. The person, the system, and the interaction must all be analyzed to understand where, why, and how errors might occur and to develop solutions that will minimize errors and lead to a safe, effective, and efficient person–­system interaction.

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ASSESSMENT CONCEPTS AND METHODS TABLE 2.2.  General List of Questions Relating to the User, the System, and the User–System Interaction Questions relating to the user General characteristics •• Who are the users? •• Is the design for a single user or for multiple users? •• What are the cultural differences between users? •• What is the average age of the intended user population? Physical characteristics •• What is the average body size of the user population? •• Do users have mobility problems that restrict normal body movements? •• What are the strength characteristics of the users? Perceptual characteristics •• What are the visual capabilities of the users? •• What are the auditory capabilities of the users? •• Do important perceptual differences exist between users? Cognitive characteristics •• What are the users’ memory capabilities and limitations? •• What are the users’ attentional capabilities and limitations? •• What decisions does the user have to make? •• What learning is required of the user? Questions relating to the system Environmental characteristics •• What are the lighting conditions of the environment? •• How much clutter is in the environment? •• How much noise is in the environment and what are its sources? •• Is the system operating indoors or outdoors? •• What is the temperature of the system environment? System characteristics •• What is the purpose of the system? •• What tasks are involved? •• Is the system automated? •• What are the system inputs and outputs? •• What sort of feedback is provided by the system? •• What instructions have been provided? •• What is the context of use? Questions relating to the user–system interaction •• What are the cognitive (memory, attention, information-processing) demands on the user? •• What are the perceptual (visual and auditory) demands on the user? •• What are the users’ experiences in relation to the system? •• What are the task demands? •• Are multiple users interacting? •• How much workload is placed on the user?

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HF/E Tools and Techniques To meet the goals of HF/E, various techniques are used to identify problem areas within a user–­system unit, describe the problems and their sources, and suggest solutions to remedy those issues. In the following sections we highlight a few techniques that are widely used throughout the field and across various domains.

Surveys and Questionnaires Surveys and questionnaires are often used in descriptive studies to gather data from the user’s perspective (see, e.g., Leonard, Jacko, Yi, & Sainfort, 2006; Stanton, Salmon, Walker, Baber, & Jenkins, 2005; Vu & Proctor, 2006). An advantage of surveys and questionnaires is that the data can be qualitative or quantitative, by asking either open-ended questions or ranking responses on a numerical scale. These methods also offer flexibility in assessing a wide range of variables, and data can be obtained from a large group of users in a relatively short period of time. However, it may be difficult to obtain a representative sample of respondents for a survey, and the development of materials and analysis of qualitative data can be time-­consuming and laborious. Moreover, both the developers’ and the users’ biases may affect the validity of the results.

Interviews and Focus Groups Interviews and focus groups can also be used to collect descriptive data from users (Stanton, Salmon, et al., 2005; Vu & Proctor, 2006). Interviews are conducted in a one-on-one environment, whereas focus groups are conducted with a moderator leading a small group, ideally with around five individuals of similar backgrounds. Interviews are appealing in that the interviewer can direct the questioning to elicit responses, especially about cognitive components of an activity. In the smallgroup environment of the focus group, ideas can emerge that may not have been realized by an individual. Although the data collected from interviews and focus groups are rich in detail and thus very informative, the analysis of such qualitative data can be challenging and time-­consuming.

Task Analysis and Process Diagrams No matter how simple a task may seem, there are often several unseen steps that a casual observer might never consider. To fully understand human–­system interactions, it is imperative that all user activities, physical or cognitive, required in a user–­system process are identified. A valuable tool for developing such a detailed understanding is a task analysis, which can be used “to identify and characterize the fundamental characteristics of a specific activity or set of activities” (Hollnagel, 2006, p. 373). Task analysis is a broad term that includes many techniques for collecting, organizing, and analyzing information about user–­system activities (for details, see Kirwan & Ainsworth, 1992). Generally an activity is selected, the goals of that activity are defined, and then there is a delineation of each step that must be performed

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to attain the final goal of the activity. Sample task analysis techniques include (1) hierarchical task analysis, wherein each task is divided into a hierarchy of subtasks with goals, operations, and plans defined; (2) link analysis, in which the relationships between a user and parts of the system are identified; (3) operational sequence analysis, for which the sequence of movements and information acceptance or dissemination are detailed, and (4) timeline analysis, wherein the time for each task element is recorded. Although a task analysis is often necessary to develop an understanding of human–­system interactions, the analysis may also require resources, such as time and video or audio equipment (Stanton, Salmon, et al., 2005). Given the detail required for an accurate task representation, it is often useful to have multiple raters analyze a task, because each analyst may create different representations of the same activity. Moreover, even within-rater reliability is not a guarantee, as an analyst may create a different representation of the same activity on different occasions. Aspects of the task analysis can be compiled into pictorial representations called “process flow diagrams.” Standardized symbols that depict the required actions, decisions, movements, information flow, time, and effort of an activity can convey the process in an easy-to-­understand format (Kirwan & Ainsworth, 1992). Figure 2.1 provides an example of a flow diagram for the relatively simple task of making coffee. The benefit of this approach is that it provides a detailed overview of the task, with every step indicated in the order in which it should be performed. Moreover a flow diagram can illustrate the actual complexity (i.e., number of steps involved) of tasks and indicate why such tasks may be overwhelming for individuals with diminished cognitive capacity. Perhaps the most important advantage of process diagrams is that an entire user–­system activity can be visualized easily without pages of text that describe each step; however, the more complex the task is, the more visually overwhelming the diagram becomes. These diagrams, which are easy to learn to create and use, can depict a range of tasks. However, they represent only one aspect of a task analysis in that they do not indicate where errors are likely to occur and the potential error sources.

Workload Analysis The task analysis and process diagrams provide general overviews of task requirements. However, the same task may impose different demands across individuals and contexts of use. Workload analysis is a means of measuring workload at the individual level. Workload can be broadly defined as “the amount of work that a machine, employee, or group of employees can be or is expected to perform” (Random House, 1998, p. 2189). Workload can be measured using physiological indices such as heart rate, measures of brain activity, or pupil dilation (Tsang & Vidulich, 2006). However, such measures may be costly or interfere with the tasks being performed. Another approach to workload analysis is to measure subjective workload. Two commonly used methods have been tested extensively for both validity and reliability: the National Aeronautics and Space Administration—Task Load Index (NASA-TLX) and the Subjective Workload Assessment Technique (SWAT).

START N Do I want coffee? Y Retrieve filter paper

Remove carafe

Place filter paper in filter basket

Pour coffee into cup

Fill carafe with water

Pour water into coffee maker

Replace carafe Turn off coffee maker

Drink coffee and enjoy Measure coffee grounds

Pour grounds into filter

Turn on coffee maker

Retrieve coffee cup Wait for coffee to brew

FIGURE 2.1.  Process diagram for making coffee.



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The NASA-TLX assesses six categories and uses the ratings to derive an overall workload score (Hart & Staveland, 1988). Users quantitatively rate six factors: mental demand, physical demand, temporal demand, performance, effort, and frustration. Advantages of the NASA-TLX are that it is quick and easy to use, and the general categories allow this technique to be applied across various domains. Disadvantages of the NASA-TLX are that the data from the six categories are complex to analyze and apply only to individual workload assessments (Stanton, Salmon, et al., 2005). Table 2.3 provides sample application of the NASA-TLX and the type of output that it provides. Note that the individual dimensions can be analyzed independently to identify the specific sources of workload for an individual. The SWAT is also a multidimensional scale like the NASA-TLX but considers different categories (Reid & Nygren, 1988). The dimensions measured are time load, mental effort load, and psychological stress load. The advantages of SWAT are similar to the NASA-TLX in that it is quick and easy to use and generalizable across domains; however, some studies have suggested that it is less sensitive than the NASA-TLX (Stanton, Salmon, et al., 2005).

Usability Assessment Tools Thus far we have described tools that are useful for understanding the user and the system. There are also techniques wherein the focus is specifically on the interaction of the user with the system. For example, usability testing can reveal critical features of the user–­system interaction. One method of usability testing is user trials, wherein users perform tasks with a product or device to evaluate features of the product and issues that may arise during use (Stanton, Salmon, et al., 2005). The flexibility and simplicity of user trials are

TABLE 2.3.  Using the NASA-TLX to Assess Subjective Workload for Two Hypothetical Diabetes Management Systems Step 1: Step 2: Step 3: Step 4:

Have patient interact with first device or system of interest Have patient complete NASA-TLX Have patient interact with second device or system of interest Have patient complete NASA-TLX System 1: Diabetes management system using directive instructions

Scale Mental Demand Physical Demand Temporal Demand Performance Effort Frustration Total workload

System 2: Diabetes management system using cooperative instructions

Value

Weight

Value

Weight

25 20 50 30 25 20

0.13 0 0.33 0.20 0.13 0.20

80 35 80 65 80 75

0.27 0 0.13 0.13 0.33 0.13

33.33

77.33

Note. The overall subjective workload is clearly lower for System 1; consequently that support system might be selected for this particular patient. However, even for System 1 the reported temporal demand is high, and the system might thus be redesigned to reduce that aspect of demand.

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an appealing advantage, but the time-­consuming nature of this technique must also be considered. Often, user trials involve a lengthy analysis because large amounts of data are collected; however, these data are extremely informative for identifying issues and assessing how the system will be used. Another approach is a “walkthrough” analysis “whereby experienced system operators perform a walkthrough or demonstration of a task or set of tasks using the system under analysis” (Stanton, Salmon, et al., 2005, p. 479). The actual system is not required in a walkthrough analysis, because the operator can simply describe the steps of the tasks performed. This technique allows assessment without interrupting real-time system operations. Although this method is very useful, its reliability is not well established because there is no prescribed technique for conducting a walkthrough analysis. Consequently it is useful to have more than one person perform the walkthrough and then to compare the results.

Knowledge Engineering Another approach to understanding the human–­system interaction, called “knowledge engineering,” can be used to identify the users, their goals, their tasks, the system, and the interaction of these components. Knowledge engineering involves developing a complete understanding of the system and its goals and then using focus groups and other knowledge acquisition techniques to elicit users’ knowledge (Bowles, Sanchez, Rogers, & Fisk, 2004). Knowledge engineering may reveal how operators actually use systems (perhaps in contrast to their intended use), how skilled operators differ from novices, gaps in operator knowledge about system functions, and information requirements for successful system use.

Developing Solutions Asking the right questions is the first step in an HF/E analysis: Who are the users, what will they be doing and in what context, what kinds of difficulties are they likely to encounter, and so on. The next step is to develop solutions. We discuss three general classes of solutions: training, environmental support, and system redesign.

Training Training the individual is one way to alleviate problems identified or to prevent problems from occurring. Training can be broadly defined as “the systematic acquisition of knowledge, skills, and attitudes that together lead to improved performance in a particular environment” (Salas, Wilson, Priest, & Guthrie, 2006, p. 473). Training can be particularly worthwhile when people are learning to use complex systems. However, there is no single training method that can be applied to all tasks. Training can include the use of instructional materials, feedback, simplification of the task, or other methods. For example, part-task training involves dividing a complex task into component tasks (Kirlik, Fisk, Walker, & Rothrock, 1998) and can be accomplished in different ways (e.g., by segmenting the task or by simplifying it). The judgment regarding the optimal approach will depend on the specific task demands. It is therefore important to conduct a training needs analysis before beginning any

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training program (Salas et al., 2006). Training needs can be identified using HF/E techniques (e.g., task analysis, knowledge engineering). Once training needs have been identified, the appropriate training technique can be implemented. Another critical component of training is the provision of feedback to guide performance and learning (Salas et al., 2006). The feedback must be timely and task relevant as well as allow the trainee to learn to adjust and improve behavior for future interactions. There is a large literature on training (for a review, see Alvarez, Salas, & Garofano, 2004) that can provide guidance for the development of training programs. One general principle to remember is that the training must be tailored to the task goals, the context of use, and the capabilities and limitations of the user (Rogers, Campbell, & Pak, 2001).

Environmental Support Another method of solving human–­system interaction problems is to provide an environmental support to aid the user in handling the cognitive aspects of a task (see Morrow & Rogers, 2008). An environmental support could be a map or outline of material on a webpage, for example, a stimulus that promotes recall of a particular characteristic, or a technological aid such as a personal data assistant (PDA). Environmental supports have proved particularly beneficial to people with limited cognitive abilities in the areas of memory and attention (Nichols, Rogers, & Fisk, 2006); these types of supports can be used to remind and guide individuals in the accomplishment of tasks, thereby improving their function in everyday situations. One way to provide environmental support—­through automation—­involves the reallocation of functions previously performed by a human to a computer or electronic device (Sheridan & Parasuraman, 2006). Automation of tasks can free up memory resources by reducing the number of items the user must remember (e.g., an automated appointment reminder on a PDA). By alerting users of when to focus attention (e.g., an alarm in the car indicating that the oil is low) instead of requiring them to sustain attention, automation can free up attentional resources. However, although automation has the potential to support aspects of everyday life, it is not a panacea. Issues such as how people’s trust in, and reliance upon, the automated device interact with system reliability, error type, and error consequence are not yet well understood (Sanchez, Fisk, & Rogers, 2006).

Redesign Human–­system interactions can also be optimized through design. For example, Rogers, Mykityshyn, Campbell, and Fisk (2001) used a task analysis to analyze a blood glucose monitor whose manufacturer claimed it was “as easy as 1, 2, 3.” However, rather than requiring 3 easy steps, there were 52 substeps to perform! Based on this analysis, Rogers and colleagues were able to provide redesign suggestions along five dimensions: (1) modify the test strips (e.g., make them longer), (2) modify the meter (e.g., reduce amount of programming required), (3) modify the features (e.g., reduce processing time), (4) modify the blood sampling procedure (e.g., reduce required

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amount of blood), and (5) modify major systems (e.g., eliminate need for calibration). Opportunities for system redesign abound (see Norman, 1988, for examples).

Summary of HF/E Tools HF/E provides guidance for understanding human–­system interactions by asking the right questions, assessing user–­system interactions, identifying problems, and providing solutions. The first step is to ask the right questions about the user and the system. What are the perceptual and/or cognitive demands on a user? What are the characteristics of the system being used? The next step is to choose an approach to answer the question. From task analyses to focus groups to usability testing, many methods can be used. However, one must carefully consider both the advantages and disadvantages of each approach. The final step is to provide a solution: Training, environmental support, and redesign are all potential solution options. We have provided only a brief introduction to the discipline of HF/E. We recommend the following texts for more details: •• Engineering Psychology and Human Performance (Wickens & Hollands, 2000) •• “Extra-­ordinary” Ergonomics: How to Accommodate Small and Big Persons, the Disabled and Elderly, Expectant Mothers, and Children (Kroemer, 2006) •• Handbook of Human Factors and Ergonomics Methods (Stanton, Hedge, Brookhuis, Salas, & Hendrick, 2005) •• Handbook of Human Factors and Ergonomics (2nd and 3rd editions; Salvendy, 1997, 2006) •• Human Factors Methods: A Practical Guide for Engineering and Design (Stanton, Salmon, et al., 2005)

Illustrative Examples The following sections illustrate the application of the HF/E methods described above. The goal of these examples is to demonstrate how HF/E has been applied to diverse domains, including everyday activities, work, health promotion, and navigation. Cultural differences are also discussed as a “person characteristic” that must be considered at all stages of analysis.

Everyday Activities Everyday activities can be broadly defined in terms of three categories: (1) Activities of Daily Living (ADLs), such as bathing, toileting, and eating, that a person must perform to live successfully by oneself (Clark, Czaja, & Weber, 1990); (2) Instrumental Activities of Daily Living (IADLs), such as housework, managing medication, and preparing nutritional meals (Lawton, 1990); and (3) Enhanced Activities of Daily Living (EADLs), which are activities that individuals perform in adapting to chang-

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ing environments (e.g., using an ATM or e-­mailing) and learning new skills to cope with these challenges (Rogers, Meyer, Walker, & Fisk, 1998). Declining cognitive and physical functioning can hamper performance of these activities, and much of the research in this domain has focused on aging. Researchers have assessed how people’s abilities change with age and how these changes impact independent functioning in the home. Despite the focus on aging in this area, the research approach is relevant to all ages.

HF/E Questions Relevant to ADLs, IADLs, and EADLs What kinds of difficulties in ADLs are encountered by a person with arthritic hands? How does this person open a jar of spaghetti sauce, insert a key into a lock, or type on a keyboard? What if a person has limited leg mobility? How would that person climb stairs, make the bed, sweep the floor, or take a shower? The physical demands of daily living activities should not be overlooked; see Clark and colleagues (1990) for a direct assessment of the physical demands required to perform various ADLs. Also relevant here is developing an understanding of the cognitive component of everyday activities. For example, a specific question relevant to EADLs might be, “What is the relationship between World Wide Web strategy use and search success for experienced younger and older users?” (Stronge, Rogers, & Fisk, 2006). Researchers have also studied the frustrations and difficulties older adults experience in the context of performing ADLs, IADLs, and EADLs (Rogers et al., 1998). Other HF/E questions relevant to these activities may include the attentional demands of cooking a meal. There may be multiple ingredients to track, events that must be sequenced properly, as well as timing of various components and monitoring to prevent burning. HF/E analysis can provide insight into these issues.

HF/E Techniques Using task analysis techniques, the physical demands associated with ADLs were detailed by videotaping older adults performing certain tasks (Clark et al., 1990). Via this method, tasks such as making the bed were divided into elemental physical units such as bending, reaching, grasping, and pulling. Although this study focused on the physical actions required of ADL tasks and the capabilities and limitations of older adults, this method can be used to assess cognitive components of ADL tasks for users of any age or ability. For instance, the cognitive demands of preparing a meal can be identified by using task analysis. This method can illustrate how an individual remembers which ingredients have been added or how he or she monitors meal preparation progress. To understand the task components of strategies in searching the Web, participants were monitored as they executed specific queries (Stronge et al., 2006). Process diagrams were created from these observations to visualize the various Web search strategies used, and knowledge engineering was used to assess the declarative knowledge of Web users. These methods provided detailed descriptions of each step in a complex process. This study illustrated different strategies and processes that can be used to successfully search the Web.

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Focus groups were conducted to collect descriptive data about the frustrations older adults encounter in ADLs, IADLs, and EADLs (Rogers et al., 1998). The questions centered on the constraints of interacting with devices and performing everyday tasks. The benefit of this method is that the group dynamic can move the conversations into a data-rich domain that the interviewer may not have considered. These HF/E methods yield valuable data relevant to solving user problems in the domain of everyday activities.

Solutions or Potential Solutions Environmental supports, assistive technologies, and support services were identified by Clark and colleagues (1990) as solutions to remedy the physical problems experienced by older adults when performing ADLs. The data collected from the focus groups indicated that nearly 40% of the problems encountered in ADLs, IADLs, and EADLs by older adults were a result of physical limitations, whereas 30% were attributable to cognitive limitations (Rogers et al., 1998). Automated aids such as the Cook’s Collage, which gives the user feedback about which ingredients have been added to a recipe, may assist those with memory deficits in the realm of everyday activities (Sanchez, Calcaterra, & Tran, 2005). Training was identified by Rogers and colleagues (1998) and Stronge and colleagues (2006) as a solution to aid older adults in EADLs. Other suggested solutions included redesign of the Internet search engines studied by Stronge and colleagues.

Work and the Workplace The workplace can be anywhere. For taxi drivers it is the car, for accountants it is an office, for golf course maintenance teams it may be riding a mower. No matter the occupation, there are several aspects of work that must be considered and assessed to foster optimal work performance. With the diversity of work and workplaces, it is important to understand the physical and cognitive aspects of a job to make it safer and its performance more efficient.

HF/E Questions Related to Work and the Workplace Physically fitting the workspace to the human operator is important to prevent injury and to increase work efficiency (Spath, Braun, & Hagenmeyer, 2006). Some HF/E questions related to physical considerations involve the diversity of size of the users. For example, in any office environment it is critical to ask how users differ in size and shape. Also, what are the physical capabilities and limitations of the users? Do any of the users have injuries or deficits that restrict movements? Other issues relate to the layout of the workspace—for example, the placement of items required for the job, such that they are physically accessible to users. Investigation of the layout leads to questions concerning the most important items and which items are most frequently used. In addition to physical considerations, the cognitive aspects of a job must be addressed. For example, consider the cognitive aspects of using a riding mower at a

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ASSESSMENT CONCEPTS AND METHODS

golf course. Relevant cognitive questions might concern the types of decisions that the operator must make while operating the mower. What cues does the operator have available on which to base those decisions? What are the memory demands of the task (e.g., things that have already been done and things that still need to be done)? To what does the operator need to pay attention? How much workload is placed on the operator while completing the task?

HF/E Techniques Knowledge engineering techniques have been applied to the analysis of commercial mowing at a golf course (Sanchez, Bowles, Rogers, & Fisk, 2006). Product manuals, subject matter experts, focus groups, and process flow diagrams were used to understand the task of mowing a golf course. The focus group data gave insight into what operators do when faced with a specific problem (i.e., slipping in wet grass), the decision sequence that takes place to solve the problem (i.e., reduce pressure on gas, lift blades, etc.), and the reasons behind the decisions. Knowledge engineering also provided insight into areas where the operators’ understanding of the system was inaccurate. This was accomplished by comparing the actions of the operators to the information available in the instruction materials. The comparison revealed that operators were unaware of the benefits of a key mower feature (i.e., the traction control knob) that was described by the subject matter expert as essential for successful operation. How a user operates a piece of machinery (e.g., a golf course mower) or makes a decision within a system is influenced by the amount of workload placed on the user. The amount of workload will differ depending on the tasks that must be completed or monitored at a given time, the complexity of the tasks, or the amount of time available to complete tasks (Gonzalez, 2005). Different individuals have different workload capacities, of course, and people with limited cognitive abilities are likely to be more affected by workload. The subjective workload associated with the task can be measured for each individual using one of the methods discussed previously (i.e., NASA-TLX, SWAT). For example, during dynamic decision-­making tasks, decisions that are made in real time and that are affected by the environment in which they are being made are negatively impacted under high workload and when individuals have limited cognitive abilities (Gonzalez). Thus, when designing tasks and jobs for individuals, it is important to examine, understand, and, if appropriate, adjust the workload placed on the user.

Solutions or Potential Solutions The knowledge engineering study conducted by Sanchez and colleagues (2006) provided insights into the potential for solution in the three categories described above (training, environmental support, and redesign). Training could help operators learn to use the mower to its maximum efficiency, for example, by teaching operators how to use the traction control system. Environmental support might be provided through automating the traction control such that it automatically engages when the mower slips. Future redesigns of the mower could make the traction control feature more salient either by emphasizing it in the instructions or by placing the control in a visible location. Other solutions might reduce or manage workload: training to improve

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the skill of the user so that the task becomes easier, environmental support to aid memory or other taxed cognitive resources, or redesign of the system to reallocate functions from the person to the machine.

Health Promotion Health improvement is an important everyday activity that can benefit from HF/E analysis. For example, medication noncompliance has been shown to be a serious problem both for the individuals as well as for the entire health care system. According to the American Heart Association (2006), “The number one problem in treating illness today is patients’ failure to take prescriptions medications correctly.” In fact, it is estimated that more than half of all Americans suffering from chronic illnesses do not adhere to their health care provider’s medication guidance. Using HF/E techniques, researchers have identified problem areas and suggested solutions to improve adherence. Medication adherence is only one area in the health domain that HF/E researchers have examined. Other areas include medical device use and nutrition label effectiveness. Much research in this domain has focused on an aging population, likely because older adults take more medications and have more health issues than younger adults. Also, as the average expected lifespan increases, people are more likely to have chronic diseases that they must manage. However, this research is relevant to all ages and all conditions because the same HF/E techniques can be used to identify issues, suggest solutions, and direct future research.

HF/E Questions Related to Health Promotion Managing one’s health is easy when one is very healthy. However, how does health management change when one is not very healthy? For instance, what are the demands of managing multiple medications when a person’s health declines? How difficult are prescription instructions to follow? How effective are cognitive aids, such as pill organizers and organizational charts, in facilitating adherence to a medication regimen (Park, Morrell, Frieske, & Kincaid, 1992)? What is the best way to train individuals to use a sequential, multiple-step device, such as a glucometer (Mykityshyn, Fisk, & Rogers, 2002)? How simple are “simple” medical devices (Rogers, Mykityshyn, et al., 2001)? How should a nutrition label be designed to ensure optimal reader comprehension (Marino & Mahan, 2005)?

HF/E Techniques Rogers, Mykityshyn, and colleagues (2001) used a task analysis to assess the physical and cognitive steps required in using a medical device. This analysis clearly demonstrated that the device (a glucometer) has multiple steps that must be performed in a specific sequence to attain the end goal of proper use. These steps can tax the working memory of the users and likely increase stress. To assess the mental workload of using a medical device, participants in the Mykityshyn and colleagues (2002) study completed the NASA-TLX after each step. This provided the researchers with a subjective measure of the mental workload placed on the users.

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Solutions or Potential Solutions Training, support, and redesign are all potential solutions to health promotion issues faced by individuals. Video training led to more successful medical device use than a text manual in the Mykityshyn and colleagues (2002) study. The video training likely provided more environmental support by minimizing working memory load and visualization demands placed on the user compared to reading a manual. By supporting comprehension, working memory, long-term memory, and prospective memory, Park and colleagues (1992) found that combining a pill organizer and an organizational chart resulted in the highest medication adherence. By following HF/E information display principles, Marino and Mahan (2005) showed that current nutrition labels are inadequate in the demands that they place on readers. They found that information integration of current labels imposed working memory demands upon readers; participants made more correct nutrition judgments when the label design was displayed pictorially. System redesign was suggested by Rogers, Mykityshyn, and colleagues (2001) when the usability testing revealed that the “user-­unfriendly” device design could not be remedied by training alone.

Getting Around: Issues of Driving and Navigation Most people think about “getting around” as simply a matter of jumping in a car and driving to a destination. However, transportation issues are also found in navigating through an environment on foot or using public transportation. In this section, we discuss not only driving and the cognitive factors involved in it, but also wayfinding and navigation.

HF/E Questions Related to Driving and Navigation Navigating through the environment or finding one’s way can be reasonably easy if a person is in a familiar environment and perceptual or cognitive resources are not being overly taxed. However, when a person is in an unfamiliar place, with the added complexities of driving, navigating the environment can become very demanding. For individuals with cognitive impairments, these problems may be exacerbated (Sohlberg, Todis, Fickas, Hung, & Lemoncello, 2005). Relevant questions then relate to understanding the capabilities and limitations of individuals with respect to the task of navigating an environment or driving a vehicle. System analysis is also critical: Which characteristics of the environment and the vehicle are placing demands on the user, and what exactly are those demands? The questions should address all aspects of navigating an environment or driving a car, from determining a route to reading street signs and from visually searching an environment for hazards to deciding to proceed through an intersection.

HF/E Techniques Task analysis indicates that three domains of ability or human functioning relate to successfully getting around: sensoriperceptual (vision and audition), cognitive (attention, memory, spatial processing), and movement control (Watson, 2001). Being able

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to see or hear is crucial to successful navigation. Vision deficiency can be problematic when navigating. The ability to read street signs and directions and the ability to adjust to differing light conditions (e.g., those that occur when going from outside to inside) are important to finding one’s way. Visual attention—that is, the visual information that can be attended to during a brief period of time—has a significant effect on the ability to drive and avoid accidents (Goode et al., 1998). Visual attention can be measured using the Useful Field of View (UFOV) test, which indicates the size of the area to which individuals can visually attend. The size of the UFOV has been shown to predict crash involvement and to assess risk of crashing in older adults who generally have smaller UFOVs (Goode et al.). Given the nature of driving today, with the demand of performing multiple tasks at a given time, the driver’s capacity to divide attention is also important. Drivers may simultaneously talk on a cell phone, adjust the radio, listen to music, or talk to a passenger (not to mention put on makeup or eat lunch). Individuals with fewer attentional resources have more difficulty performing multiple tasks successfully and may put themselves and others at risk by attempting to do so (Caird, Edwards, Creaser, & Horney, 2005). Research shows that when attention is divided while driving, people react more slowly, show greater speed variation, follow at a greater distance, and are involved in more rear-end collisions (Strayer & Drews, 2004). In addition, when attentional resources are being taxed by, for example, searching for objects in the environment, such as street signs and other vehicles, drivers are slower and less accurate (McPhee, Scialfa, Dennis, Ho, & Caird, 2004). Dividing attention can leave less reaction time for evasive action (McPhee et al., 2004) and negatively impact decisions while navigating complex environments such as intersections (Caird et al., 2005). When attention is taxed, individuals based driving decision on fewer cues in the environment. To assess these effects for individuals from specific populations, surveys, interviews, and focus groups with populations of interest can be used. These methods are an excellent way of ascertaining the source and subsequent outcomes of many functional limitations associated with navigation and wayfinding. For example, Sohlberg and colleagues (2005) used these techniques to assess the challenges faced by cognitively impaired individuals. These individuals expressed concerns about getting lost and the challenges associated with problem solving while en route; their concerns resulted in fewer medical and business visits and reduced social interaction. The interviews and focus groups enabled the researchers to delve more deeply into the individuals’ problems and their reported strategies to overcome them. These data indicated potential areas for solutions to these problems.

Solutions or Potential Solutions Human beings are very good at adapting to situations and overcoming obstacles—up to a point. Although some people may experience problems navigating an environment, many develop “survival” strategies. Sohlberg and colleagues (2005) found that people often use explicit written directions received in advance to reduce memory demands. Landmarks were found to be unhelpful because, when memory is a problem, cognitively impaired individuals do not remember having passed landmarks. The authors also found that it is important for cognitively impaired individuals to

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have backup plans if the primary strategy fails (e.g., if the directions are lost). Such plans include asking people for directions or carrying a cell phone to receive directions from family or friends. These survival strategies can inform our solutions to navigational problems. Training has proved effective for improving problems associated with UFOV and risk awareness in drivers. For example, the size of UFOV was expanded when participants were trained on speed of processing, a fundamental ability influencing UFOV (Roenker, Cissell, Ball, Wadley, & Edwards, 2003). Risky driving behavior was decreased by training inexperienced drivers to reduce their exposure to dangerous situations (Fisher et al., 2002). Furthermore, training individuals to focus attention on appropriate locations in complex driving situations may enable them to compensate for taxed attentional resources.

Cultural Considerations: Globalizing HF/E With the increased cultural interactions in the present global economy, considering users with cognitive, perceptual, and motor differences outside of Western society is essential for the acceptance and integration of systems and technology worldwide. Important cultural distinctions—­cognitive, perceptual, and physical—may be relevant to proposed HF/E solutions. Anthropometric data used by human factors specialists are based primarily on measurements derived from Western populations. However, there are significant physical differences between cultures. For example, on average, Japanese people are shorter than people from Western countries (Lippa & Klein, 2005). A mismatch between the physical size of users and the physical size of where or what they are operating can lead to reduced efficiency in the workplace and increased safety risks. Cultural differences can also be seen in differences of perception. Culture—­ defined by Webster’s (2006) as the belief system and values of the society in which one is raised—can have a significant influence on how one perceives the world. A study of Nepalese people showed that they exhibited significantly higher pain thresholds compared to Western people (Clark & Clark, 1980), which were not attributed to neurosensory variations but to differences related to pain-­reporting criteria, brought on by a different cultural value system. It is therefore important to be cognizant of cultural differences in the context of human–­system interaction. These differences might impact measures of subjective workload, for example. Research also indicates that there are cultural differences in the way we think. For example, a comparison of Asian and American cultures revealed distinct differences in the way each uses intuition versus formal reasoning to overcome conflict (Norenzayan, Smith, Kim, & Nisbett, 2002). Americans were more likely to use formal reasoning compared to Chinese and Koreans, who relied more heavily on intuitive strategies for solving conflict. Nisbett, Peng, Choi, and Norenzayan (2001) found that Westerners were more analytical compared to East Asians, who tended to be more holistic in their systems of thought. These fundamental cultural differences can influence cognition and motivation. In a study comparing American and Asian cognitive styles, Rau, Choong, and Salvendy (2004) found that American cognitive style tends to classify stimuli based on inferences about those stimuli or their functions. In contrast, some Asian cultures

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tend to classify stimuli based on their interrelationships. The Asian way of thinking tends to be more relational compared to American way, which tends to be more analytical. The result is that Asian cultures rely on experience and do not separate the person from objective facts, whereas Americans do separate subjective experience from objective reality. An implication of cultural differences in cognition is that the mental models on which designs are based may not work for people from other cultures and, in fact, may be detrimental to their efficiency and safety when interacting with the product/system. Cultural differences can play a big role when applying HF/E tools and techniques. For example, Chavan (2005) pointed out that the Indian culture generally accepts the current state of a situation and then looks for ways around it. This mental set can pose a problem when conducting usability studies in that people from the Indian culture do not like giving negative opinions. In addition, people from collectivist cultures (e.g., most Asian cultures) may have trouble providing an individual opinion and will likely give an opinion that he or she thinks the collective would hold. Therefore, when applying HF/E tools it is important to consider cultural differences and how they affect the collection and interpretation of data.

Summary of Illustrative Examples As the above examples illustrate, HF/E techniques have been used in a wide range of domains. From task analysis to focus groups and from driving a car to managing medications, HF/E techniques have been used to identify problem areas; describe the user, the system, and their interaction; and suggest solutions. The advantage of these tools is that they can be applied to any domain or any system that involves a human user. HF/E techniques consider the user’s capabilities and limitations and the context in which the user is interacting with the system.

Looking to the Future The discipline of HF/E has much to offer the practice of neuropsychology. In fact, there is an emerging area called “neuroergonomics” that represents the intersection between HF/E and neuropsychology (see Parasuraman, 2003). Neuroergonomic analysis involves understanding the neural bases of perception and cognition as they relate to human–­system interactions. Parasuraman provided examples of how this approach may involve assessments of cognitive workload, attention, and oculomotor control. Such measures may prove useful to detect when workload is overloading for individual patients, for example, while they are performing a particular task. Neurological measures would be particularly useful if the person were unable to provide an accurate report of subjective workload. The concept of adaptive automation also has potential for supporting patients’ needs. In adaptive automation, functions are assigned (allocated) either to the system or to the person, based on different parameters such as workload, stress, or ability. For example, in a low stress or low workload situation it may be desirable to have the human perform a task (e.g., wayfinding) so that he or she can maintain and possibly improve functional abilities. However, in high-­stress or high workload situations it

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might be critical to have an automated system provide the needed information. This type of adaptive system could support patient’s learning during the rehabilitation process yet recognize situations of overload and provide technological support as needed. Adaptive automation is reliant on valid and timely assessments of workload that might be attained through neuroergonomic assessments—­measuring brain function during task performance. Such measures can be continuous and nonintrusive. According to Parasuraman (2003), “Measures of brain function can indicate not only when an operator is overloaded, drowsy, or fatigued, but also which brain networks and circuits may be affected. In short, neuroergonomic measures offer new avenues for adaptive interventions aimed at enhancing system performance” (p. 12). The broad discipline of HF/E has well-­developed methods to enable understanding of human–­system interactions in a variety of contexts. These methods provide ways of asking questions that lead to the development of solutions through training, provision of environmental support, or system redesign. Such solutions can be implemented for groups of people or for single patients—in either case these solutions have the potential to improve the safety, efficiency, and effectiveness of human–­system interactions.

Acknowledgments We were supported in part by Grant No. P01 AG17211 from the National Institutes of Health (National Institute on Aging) under the auspices of the Center for Research and Education on Aging and Technology Enhancement (CREATE). Order of the second and third authors is random—they contributed equally.

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Norman, D. (1988). The psychology of everyday things. New York: Basic Books. Parasuraman, R. (2003). Neuroergonomics: Research and practice. Theor Issues Ergon, 4, 5–20. Park, D. C., Morrell, R. W., Frieske, D., & Kincaid, D. (1992). Medication adherence behaviors in older adults: Effects of external cognitive supports. Psychol Aging, 7, 252–256. Random House Webster’s unabridged dictionary (2nd ed.). New York: Author. Rau, P. P., Choong, Y., & Salvendy, G. (2004). A cross cultural study on knowledge representation and structure in human computer interfaces. Int J Ind Ergonom, 34, 117–129. Reid, G. B., & Nygren, T. E. (1988). The subjective workload assessment technique: A scaling procedure for measuring mental workload. In P.A. Hancock & N. Meshkati (Eds.), Human mental workload (pp. 185–218). New York: Elsevier. Roenker, D. L., Cissell, G. M., Ball, K. K., Wadley, V. G., & Edwards, J. D. (2003). Speed-of­processing and driving simulator training result in improved driving performance. Hum Factors, 45, 218–233. Rogers, W. A., Campbell, R. H., & Pak, R. (2001). A systems approach for training older adults to use technology. In N. Charness, D. C. Park, & B. A. Sabel (Eds.), Communication, technology, and aging: Opportunities and challenges for the future (pp. 187–208). New York: Springer. Rogers, W. A., Meyer, B., Walker, N., & Fisk, A. D. (1998). Functional limitations to daily living tasks in the aged: A focus group analysis. Hum Factors, 40, 111–125. Rogers, W. A., Mykityshyn, A. L., Campbell, R. H., & Fisk, A. D. (2001) Analysis of a “simple” medical device. Ergon Des, 9, 6–14. Salas, E., Wilson, K. W., Priest, H. A., & Guthrie, J. W. (2006). Design, delivery, and evaluation of training systems. In G. Salvendy (Ed.), Handbook of human factors and ergonomics (3rd ed., pp. 472–512). Hoboken, NJ: Wiley. Salvendy, G. (1997). Handbook of human factors and ergonomics (2nd ed.). New York: Wiley. Salvendy, G. (2006). Handbook of human factors and ergonomics (3rd ed.). Hoboken, NJ: Wiley. Sanchez, J., Bowles, C. T., Rogers, W. A., & Fisk, A. D. (2006). Human factors goes to the golf course: Knowledge engineering of commercial mowing. Ergon Des, 14, 17–23. Sanchez, J., Calcaterra, G., & Tran, Q. Q. (2005). Automation in the home: The development of an appropriate system representation and its effects on reliance. In Proceedings of the Human Factors and Ergonomics Society 49th annual meeting (pp. 1859–1862). Santa Monica, CA: Human Factors and Ergonomics Society. Sanchez, J., Fisk, A. D., & Rogers, W. A. (2006). What determines appropriate trust of and reliance on an automated collaborative system?: Effects of error type and domain knowledge. In Proceedings of the 9th international conference on control, automation, robotics, and vision (pp. 98–103). New York: IEEE. Sheridan, T. B., & Parasuraman, R. (2006). Human automation interaction. In R. S. Nickerson (Ed.), Reviews of human factors and ergonomics (pp. 89–129). Santa Monica, CA: Human Factors and Ergonomics Society. Sohlberg, M. M., Todis, B., Fickas, S., Hung, P., & Lemoncello, R. (2005). A profile of community navigation in adults with chronic cognitive impairments. Brain Injury, 19, 1249–1259. Spath, D., Braun, M., & Hagenmeyer, L. (2006). Human factors and ergonomics in manufacturing and process control. In G. Salvendy (Ed.), Handbook of human factors and ergonomics (3rd ed., pp. 1597–1625). Hoboken, NJ: Wiley. Stanton, N. A., Hedge, A., Brookhuis, K., Salas, E., & Hendrick, H. (2005). Handbook of human factors and ergonomics methods. Boca Raton, FL: CRC Press.

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Chapter 3

Occupational Therapy Approach to Assessing the Relationship between Cognition and Function Carolyn M. Baum and Noomi Katz

O

ccupational therapy is concerned with enabling the individual to do the activities, tasks, and roles that are self-­identified as necessary for daily life. This approach, known as the person, environment, and occupation (PEO) approach, requires scientists to discover and then clinicians to address in practice the relationship among person factors (psychological, cognitive, sensory, motor, physiological), occupations (what the individual needs and wants to do to maintain self and engage in work, family, and community activities), and environment social support (social capital, the physical environment, and culture).

The Occupational Therapy Approach There are several contemporary PEO models in the occupational therapy literature: the person–­environment–­occupational performance model (Christiansen & Baum, 1991, 1997, 2005); the model of human ecology (Dunn, Brown & McGuigan, 1994; Dunn, Brown, & Youngstorm, 2003); the model of human occupation (Kielhofner, 2002, 2008); the person–­environment–­occupation model, (Law et al., 1996); the Canadian model of occupational performance (Townsend et al., 1997); and occupational performance—­Australia (Chapparo & Ranka, 1997). Each of these models includes the three central elements—­person, occupation, and environment—and each acknowledges the importance of the stages of development as they influence motivation, skills, and roles. Moreover, they share views of the individual that emphasize the complex relationship of biological, psychological, and social phenomena and the importance of a satisfactory match between the person, the task, and the situational characteristics. This interaction is known as “occupational performance,” the term occupational therapists use to describe the function of an individual interacting with the environment while doing the activities that are important for him or her to do. 62

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The PEO concepts were articulated by scholars in occupational therapy throughout the 20th century (Fidler & Fidler, 1973; Meyer, 1922; Mosey, 1974; Reilly, 1962) and form the basis for views of occupational therapy practice that address the occupational performance issues of individuals. The PEO models are based on research and knowledge from the behavioral and social sciences (such as psychology, anthropology, and sociology), the neurosciences, as well as from work in newer areas such as rehabilitation science, disability studies, and occupation science. Each provides a unique perspective and emphasizes the concepts differentially but based on the same foundation. Occupational therapy intervention is viewed as a process of using a broad range of purposeful client-­centered strategies that engage the individual to develop or use his or her resources to enable successful performance. The satisfactory performance of occupations is seen as a consequence of individual goals in relation to environmental characteristics that either limit or support an individual’s participation. Intervention strategies may or may not involve an individual’s direct engagement in occupation. In some cases, it is possible to modify environments to make them accessible and manageable. The client’s active involvement may consist of working with the therapist and the family to identify goals and strategies that will remove barriers and enable participation in tasks and roles. Occupational therapists almost never do things to people; they more frequently do things with people. Many people with chronic health conditions and disabilities have cognitive problems that limit their performance in daily life activities. Moving through daily life requires the individual to formulate goals, plan how to achieve them, and carry out plans (Lezak, Howieson, & Loring, 2004). Occupational therapists work with children and adults who have difficulties formulating and maintaining the focus on their goals. Goal-­directed activities include caring for self and others; maintenance of the home; work, fitness, leisure, and sport activities; as well as community, social, and spiritual activities. These types of activities give meaning to people’s lives. Implementing goal-­directed activities requires individuals to use higher-level cognitive processes to self-­correct, make decisions, use judgment, and make wise choices as they navigate through life’s challenges and difficulties (Goel, Grafman, Tajik, Gana, & Danto, 1997; Lezak, 1982; Lezak et al., 2004). Impairment or loss of these abilities compromises individuals’ ability to participate fully in society. Emphasis on both occupational performance activities and participation in the community requires the practitioner to employ a client-­centered strategy (Baum & Law, 1997; Fisher, 1998; Mathiowetz & Haugen, 1995). The practitioner must first determine with the client what he or she perceives to be the issues that are limiting his or her participation and causing difficulty in carrying out tasks related to productivity and work, personal care, home maintenance, sleep, recreation, and/or leisure. This approach is defined as a top-down one because it starts with the individual’s performance to identify any physiological, psychological, cognitive, neurobehavioral, and/or spiritual factors that may be interfering with, or supporting, the individual’s performance; as well, it identifies the environmental factors that may serve as enablers or barriers to performance. It is important to determine an individual’s capacity for real-world everyday performance (Alderman, Burgess, Knight, & Henman, 2003; Allen & Blue, 1998; Allen, Earhart, & Blue, 1992; Fisher, 1998; Giles, 2005; Gioia & Isquith, 2004; Keil &

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Kaszniak, 2002; Levy & Burns, 2005; Shallice & Burgess, 1991). Real-world performance requires multi­tasking that occurs in environments that may or may not be supportive. Testing of real-world performance requires behavioral observations in the context in which the individual will be required to do the tasks (Burgess et al., 2006). The information obtained by occupational therapists from such assessments enables them to work with individuals and their families to maximize function in those with cognitive loss. Occupational therapists assess cognition to determine individuals’ capacities to live alone safely and comfortably, to work, or to do any task that is important and meaningful for them. They also address the impact that executive function has on performance. By assessing cognitive capacities in the performance of daily tasks, it is possible to determine their strengths, limitations, and challenges as they learn skills and environmental strategies that support them in their daily lives. It is possible to observe key executive constructs in the performance of daily life tasks even in those with mild cognitive deficits (Bar-Haim Erez, Rothschild, Katz, Tuchner, & Hartman-Maeir, in press; Baum & Edwards, 1993; Burgess et al., 2006; Edwards, Hahn, Baum, & Dromerick, 2006). These tasks include initiation, the process that precedes the performance of a task; organization, the physical arrangement of the environment, tools, and materials to facilitate efficient and effective performance (Katz & Hartman-Maeir, 2005; Lezak et al., 2004); judgment (Goel et al., 1997; Lezak, 1982); and completion (Baum et al., 2008; Goel et al., 1997). Occupational therapists measure cognition and function not just to know what a person can do, but to know what to do to foster that individual’s engagement in daily life—­because occupation is a basic human need, a determinant of health, and a source of meaning (Katz, 2005; Townsend, 1997).

Occupational Therapy Cognitive Theoretical Models The last decade has seen the development of occupational therapy treatment models for persons with cognitive loss (Katz, 2005). Two major approaches are discussed here. The first, strategy learning and awareness, reviews the work of Toglia (a dynamic interactional approach to cognitive rehabilitation) and Polatajko (cognitive orientation to daily occupational performance [CO-OP]). The second approach, adaptive and functional skill training, reviews the work of Giles (a neurofunctional approach) and Levy and Burns (the cognitive disabilities model for rehabilitation in dementia) based on the work of Allen (1985; Allen et al., 1992).

A Strategy Learning and Awareness Approach Theoretical Foundations for a Dynamic Interactional Model of Cognition Traditional cognitive rehabilitation approaches have been guided by the assumption that cognition can be divided into subskills (Trexler, 1987). Toglia (1993, 2005) proposes an alternative to syndrome-­specific approaches and encourages clinicians to discover the underlying conditions and processing strategies that influence performance. Treatment focuses on helping the person become aware of how deficits require the modification of activity demands and the environment and developing

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strategies that accomplish these modifications. The approach is based on the cognitive and educational psychology literature as well as on neuropsychology, cognitive rehabilitation, and neuroscience theories that address how people process, learn, and generalize information (Toglia, 1991, 2005; Toglia & Kirk, 2000). Lidz (1987) defines cognition as the capacity to acquire and use information to learn and generalize. Individuals must take in, organize, assimilate, and integrate new information with previous experiences; adaptation involves using information that has been previously acquired to plan and structure behavior for goal attainment under changing conditions. Using this definition, cognition is not divided into subskills such as attention, memory, organization, or reasoning. Instead the approach requires understanding the person’s ability to use strategies, monitor performance, and learn. Such an approach is necessary because cognition is neither static nor stable; interaction with the external world requires such flux (Feuerstein & Falik, 2004; Lidz, 1987; Lidz & Elliot, 2000). This model conceptualizes cognition as an ongoing product of the dynamic interaction among the person, the activity, and the environment and as modifiable under certain conditions. Because there is a fixed or structural limit in the capacity to process information, there are differences in the way that capacity can be used. The same activity can require different amounts of processing capacity depending on how it is performed; thus it must be used efficiently. The efficient allocation of limited processing resources is central to learning and all forms of cognition (Flavell, Miller, & Miller, 1993). The multicontext approach is directed at teaching the person to use strategies and to self-­monitor performance, while simultaneously adapting the activity and environmental demands to be at a level slightly above the “just right challenge” (Toglia, 2005). This treatment approach focuses on the person’s use of strategies and level of awareness in graded transfer levels from near to far transfer. The approach requires the occupational therapist to present opportunities for the individual to experience different environments, different levels of demands in the activity, and to bring to consciousness a new level of awareness. The measurement methods developed for this model include a dynamic graded cueing approach to assess the current abilities of the individual, the potential performance with mediation, as well as steps to identify the level of awareness for the performance requirements within the assessments (see Table 3.4, on page 74).

Theoretical Foundations for CO-OP CO-OP is a client-­centered, performance-based, problem-­solving approach that enables skill acquisition, generalization, and transfer of learning through a process of strategy use and guided discovery (Polatajko & Mandich, 2004, 2005). The foundational theories are drawn from behavioral and cognitive psychology, movement science, and occupational therapy. Behavioral theories focus on the relationship between stimulus, response, and consequence, and learning is viewed as a permanent change in the form, duration, or frequency of a behavior. Reinforcement is seen as an integral component of learning. CO-OP uses reinforcement, modeling, shaping, prompting, fading, and chaining techniques to support skill acquisition (Polatajko & Mandich, 2004) and also builds on a cognitive view of learning as an active mental process of acquiring, remembering, and using knowledge. The mental organization of knowledge (problem solving, reasoning, and thinking) plays an important role in the acquisition and performance of skills (Schunk, 2000).

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The problem-­solving strategy used in CO-OP—“Goal–Plan–Do–Check”—was adopted from Meichenbaum (1977, 1994) as a framework for guiding the discovery of self-­generated domain-­specific strategies that support skill acquisition. After an individual chooses a goal he or she wants to accomplish or a skill to learn, the clinician guides the client in the use of the Goal–Plan–Do–Check strategy, first to determine where task performance is breaking down, and then to identify the strategies that can be used to overcome the breakdown and perform the task. For example, a 42-year-old man with a right hemiplegia wanted to be able to play cards with his friends again. By attempting to shuffle, he was guided to discover what was not working for him. He figured out that the way he wanted to shuffle required both hands to be equally active, which could not work, so he tried other approaches to shuffling. Using this method, he created an approach that both worked for him and was acceptable to him. In his new method he used his weaker hand only to assist in shuffling the cards. Though this is a single task, with it he learned how to set the goal, develop the plan, implement the strategy, and evaluate the strategy (in this case it helped him to accomplish his goal). CO-OP fosters the learning of skills that support occupational performance. The actual performance of tasks requires motor skills, and this component requires us to consider theories of motor learning. Motor learning is an internal processes that leads to a change in the learner’s capacity for skilled motor performance (Rose, 1997). The process of learning a new skill is not observable but can be inferred by observing the individual’s motor performance. The learning of a motor skill also requires the individual to interact with the environment in which the task is performed. Dynamic systems theory emphasizes the relationship between the person and the environment (Thelen, Kelso, & Fogel, 1987; Turvey, 1990); behavior is seen as arising from a hierarchical, dynamic interaction of the sensory, motor, perceptual, and anatomical systems (Thelen, 1995). The Fitts and Posner (1967) three-stage model of motor learning provides theoretical support for CO-OP. The cognitive stage guides the individual as he or she seeks to understand the task and how to perform it; in the associative stage the individual focuses attention and performs with greater speed and precision; and in the autonomous stage the skill is performed consistently and in a coordinated pattern. CO-OP is based on a learning paradigm that acknowledges that new skills emerge from an interaction with the environment; the occupational therapist, in turn, creates the learning environment to support optimal learning. In this approach cognition acts as the mediator between the individual’s ability and the performance that is the goal of the individual; as such a certain level of cognitive abilities is required to develop the new skills. Such an approach creates a learning paradigm that helps the individual develop skills that can then be used to accomplish multiple tasks that support their daily occupations.

Functional Skill Training Theoretical Foundations for the Neurofunctional Retraining Approach The aim of the functional skill training approach is to enhance abilities and participation by providing training in each activity the person with severe cognitive impairment needs to perform and modifying the activity demands and contexts. This

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approach helps the individual develop habits and routines by retraining him or her in the use of real-world skills with the goal of developing behavioral automaticity and a greater reliance on the environment, including cueing (Giles, 2005). Clients are trained in behavioral routines when there is little expectation of a generalized application of strategies to novel circumstances encountered in the real world. For example, a person might be trained to accomplish a specific morning routine by repeating the sequence of activities over many days until it becomes automatic. Or a person could be trained to walk to the same restaurant on the same route with specific points indicating where to turn, how to look before crossing a street, how to obey street lights, and so on. Once arriving at the restaurant, where there would be familiarity with the staff, the person would be trained in how to use the menu and order a dish. The aim is to help the individual learn how to maintain a schedule that becomes routine, with the same activities done in the same sequence each day. Neurofunctional retraining considers the person’s learning capacity in the design and implementation of programs. Memory, attention, and frontal lobe impairments create problems for individuals that make it difficult for them to achieve independence. Though specific cognitive capacities are not the target of direct interventions, the capacities must be considered in the design of functional skill training. Memory obviously is central to performance because the individual must remember to actually execute the skilled behaviors. By knowing which memory systems are affected, it is possible to improve performance. For example, nondeclarative (procedural) memory supports performance in that it is central to habituation and learning can occur without the client’s awareness (Giles, 2005). Attention is necessary to sustain performance of appropriate tasks and to orient the individual to surroundings and safety. Cognitive control is central to the acquisition of new learning (Schneider, Dumais, & Shiffrin, 1984) and to both focus attention and divide it when multitasking is required (Stuss et al., 1989). If individuals have a divided attentional deficit, they may have insufficient attention to do more than one thing at a time; even walking and listening to someone speak to them may cause them to lose their balance (Giles, 2005). Because task performance is influenced by competing demands on attention (Kewman, Yanus, & Kirsch, 1988), a client’s ability to sustain and divide attention under varying conditions must be understood. Impairments in conscious decision-­making capacities of the executive function may contribute to increased environmental dependence (Lengfelder & Gollwitzer, 2001). To support daily life it is important to help the person integrate awareness into the interventions. Deficits in the individual’s memory, attention, and executive function create constraints that must be overcome as the occupational therapist addresses the occupational performance needs of the person with brain injury. The neurofunctional approach considers these constraints in the development of treatment programs that train individuals to perform routines and make use of environmental affordances that support their daily life functioning. The behavioral treatment approach used in this model is also in line with an errorless learning training method, wherein errors are prevented as much as possible (Wilson, Baddeley, Evans, & Shiel, 1994; Ylvisaker, Hanks, Johnson-Green, 2003), compared to a trial-and-error learning model wherein errors are corrected. The model uses a range of assessment techniques for initial screening of neurofunction, specifically in the areas of metacognition, atten-

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tion, memory, and executive functions (see instruments in Tables 3.3 and 3.4, on page 74), however, the primary mode of evaluation is observation in real-life functioning according to the needs of the client.

Theoretical Foundations for the Cognitive Disabilities Model of Rehabilitation in Dementia Occupational therapists work with individuals and their families who are dealing with the consequences of dementia. The cognitive disabilities model uses an information­processing approach to uncover the patterns of impaired and preserved cognitive functions that impact occupational performance of the individual, in turn impacting the family (Levy & Burns, 2005). The cognitive psychology and occupational therapy frameworks provide the knowledge to inform clinical interventions. Individuals with dementia have limitations in sensoriperceptual memory, thus limiting their longerterm storage of information. Initially the decline in sensoriperceptual memory was thought to be of limited practical significance (Craik & Jennings, 1992; Mendez, Mendez, Martin, Smyth, & Whitehouse, 1990); however, studies have found that an impairment in visual sensory abilities interferes with the perception and storage of visual cues that contribute to visuospatial abilities (Tetewsky & Duffy, 1999). Because the sensoriperceptual memory store represents visual and auditory memory,it is affected by the visual and auditory deficits experienced by older adults, making it necessary to determine if a visual or hearing problem requires accommodation in order to deliver the needed visual or auditory cues. Working memory is important for the maintenance and manipulation of information. The central role of working memory is to reduce the individual’s reliance on automatic actions and to allow for the mental representation of alternatives (Goldberg, 2001). Working memory is critical to numerous functions: conscious attention, concentration, the overriding of automatic reactions when needed, moving from concrete to abstract concepts, understanding language, setting goals, planning, problem solving, decision making, and carrying out meaningful activities. Most everyday tasks, including those involved in leisure/fitness and social activities, are very dependent on working memory. As working memory becomes progressively limited, well­established long-term memories may be activated from internally generated thought processes or those cued by stimuli in the environment. These long-term memories, which can be language-based, visuospatial, visuoperceptual, and/or procedural, deteriorate in a reversed ontogenetic order (Levy, 1974, 1986; Reisberg, Franssen, Souren, Auer, & Kenowsky, 1998; Reisberg, Kenowsky, Franssen, Auer, & Souren, 1999; Reisberg et al., 2002). Old memories may be recalled but more recent memories may remain inaccessible (Haist, Gore, & Mao, 2001; Lopez, 2000). These principles of retrogenesis (Reisberg et al., 1998, 1999, 2002) echo theoretical work developed in the original cognitive disabilities model literature (Allen, 1985; Allen et al., 1992; Levy, 1974, 1986; Levy & Burns, 2005). The hippocampus, a primary site for new memories, is the first site of deterioration in Alzheimer’s disease. Because there is an inability to store new memories, the individual experiences each event as a new one, causing family members great difficulty; he or she cannot report any recent experience, including whether they have gone to the bathroom or what they have just eaten. The progression of the disease to

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areas responsible for spatial orientation, recognition of objects, and body information causes even more limitations in the performance of daily life activities. The areas that seem to be somewhat spared are the cerebellum and basal ganglia, which are the sites of procedural and other implicit-type memories. Interventions are required to help individuals maintain routines—those tasks and activities they have consistently performed over long periods of time (Averbuch & Katz, 2005; Baum & Edwards, 1993; Baum, Edwards, & Morrow-­Howell, 1993; Camp, Foss, O’Hanlon, & Stevens, 1996). Indeed, there is growing evidence that even severely impaired people with Alzheimer’s disease can learn and retain procedural tasks for at least 1 month (Camp et al., 1996; Dick et al., 1996). When procedural memories are cued (activated) under constant practice conditions, procedural skills can be retrieved by reactivating the relevant synapses in the brain. The act of retrieval appears to have a mnemonic effect. This learning is task and situation specific, and generalization is limited (Levy & Burns, 2005); however, it does offer a strategy to capitalize on for training. Recall deficits occur earlier in the disease than recognition deficits because recall is a demanding working memory task. Memories remain inactive until a cue acts as a reminder and causes them to be activated and retrieved. The cognitive disabilities model employs environmental cues to compensate for the decline in working and explicit memory. It capitalizes on procedural (implicit) and cued recognition ­capacities (episodic and semantic), using what remains to retrieve information; such a strategy enables individuals to optimize their occupational performance using the capacities that remain (Levy & Burns, 2005). It also provides strategies for the family to use to maximize their loved one’s potential to successfully interact with others. The cognitive disabilities model was originally developed by Allen for patients suffering from long-term psychiatric disabilities, then further extended to use with the dementia population and their caregivers. It provides a hierarchy of six cognitive functional levels to analyze and understand the client’s level of performance. Its measurement approach emphasizes the assessment of cognitive functional abilities in daily task performance (see Tables 3.2, on page 71, and 3.3, on page 74) and a corresponding activity analysis. Levy and Burns (2005) use the same hierarchy to explain the information processing and occupational behavior that occur at each level. The evolving exploration of brain and behavior has provided knowledge for occupational therapists to apply in working with clients who have developmental, acquired, or chronic debilitating cognitive conditions. The next step in our efforts to support and expand cognitive rehabilitation is to link brain function, behavior, and performance/participation in daily life to more accurately evaluate the effectiveness of intervention strategies. Individuals’ real-world performance can then inform our knowledge of the function and structure of the brain.

Evaluation Process for Individuals with Suspected Cognitive Disabilities The purpose of occupational therapy intervention for individuals with suspected cognitive disabilities is to minimize their limitations and enhance their participation in everyday activities (Toglia, 2003). The knowledge base underlying occupational therapy cognitive models comes from recent cognitive/neuroscience developments of rehabilitation theories and techniques that target the underlying mechanisms of the

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deficits; and psychosociobiological and holistic approaches that examine the relations between occupational performance and participation in the community and define intervention outcomes. The practitioner must begin intervention by collecting information. One system for this process, the Cognitive Functional Evaluation (CFE; Hartman-Maeir, Katz, & Baum, 2009), provides the following: (1) a description of cognitive strengths and weaknesses (lower- and higher-level cognitive functions) and their impact/implications on occupational performance; (2) a recommendation concerning the type and amount of assistance required for safe and meaningful occupational performance; and (3) clinical reasoning to guide the practitioner in selecting the treatment method (e.g., considering the severity of deficits, learning ability, degree of awareness, variation in skills, environmental conditions, occupational goals) that will be employed and in providing the family with the knowledge and skills to support successful interactions with their loved one.

Stages in the Development of a CFE The development of a CFE proceeds through the following stages: 1. Interview and background information, including an occupational history. 2. Cognitive screening and baseline status tests. 3. Measures of cognition in task performance. 4. In-depth testing of specific cognitive domains: laboratory-like tests. 5. In-depth assessment of performance in functional activities. 6. Evaluation of environmental/contextual factors.

Interview and Background Information In persons with suspected cognitive deficits, it cannot be assumed that they have a realistic view of their condition; therefore the first step is to determine their level of awareness. There are different methods to evaluate awareness, including the use of interviews with questionnaires, comparison between the answers of the individual and a proxy (e.g., relative, other caregiver, therapist), comparison to test performance, and prediction before and evaluation after task performance (Katz & Hartman-Maeir, 2005; Katz, Hartman-Maeir, Ring, & Soroker, 2000; Prigatano, 1986; Toglia, 1993, 2005). We recommend the Self-­Awareness of Deficits Interview (SADI; Fleming, Strong, & Ashton, 1996) at this first stage. Following evaluation of the person’s self-­awareness, information about occupational history and activities performed in daily functioning is obtained to determine his or her interests and experience with activities that can be used to build daily routines. This information is obtained with an Occupational Questionnaire (Smith, Kielhofner, & Watts, 1986) that lists activities during a typical 24-hour day, and the Activity Card Sort (ACS) for adults (Baum, 1995; Baum & Edwards, 2001, 2008; Katz, Karpin, Lak, Fuman, & Harman-Maeir, 2003; Sachs & Josman, 2003) or the Pediatric Activity Card Sort for children (Mandich, Polatajko, Miller, & Baum, 2004). To complete the initial interview the Canadian Occupational Performance Measure (COPM; Law et al., 1998) is administered to gain understanding of the client’s occupational goals and the tasks of his or her choice (see Table 3.1).

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TABLE 3.1. Components of the Initial Interview •• Self-Awareness of Deficits Interview (SADI; Fleming et al., 1996) •• Brief occupational history; Occupational Questionnaire, 24 hours of a typical day (Smith et al., 1986), www.moho.uic.edu; Model of Human Occupation clearinghouse-related resources •• Activity Card Sort (ACS; Baum 1995; Baum & Edwards, 2001, 2008) •• Canadian Occupational Performance Measure (COPM; Law et al., 1998)

Cognitive Screening and Baseline Status Tests In order to acquire a basic knowledge of the cognitive abilities and deficits of the client, the occupational therapist administers the appropriate tests depending on the client’s age, diagnosis, stage of illness, setting, and so on. The instruments listed in Table 3.2 are standardized, and their psychometric properties were established with various populations. The Mini-­Mental State Examination (MMSE; Folstein & Folstein, 1975) and the Short Blessed Test (Katzman et al., 1983) are used extensively as screening tools for dementia, and clock drawing tests are used in a variety of ways to assess visual–­ spatial deficits, spatial organization, memory, and executive functions (Freedman et al., 1994; Royall, Cordes, & Polk, 1998). The Allen Cognitive Level Screen–5 (ACLS-5) and the Large ACLS-5 (LACLS5) are short screening tests using a leather lacing task with three different stitches graded in difficulty from one step at a time to two steps and error corrections. A cognitive function score between 3 and 6 is provided, as is a more refined score ranging from 3.0 to 5.8. Extensive research to support the reliability and validity has been done with this instrument (Allen et al., 1992, 2007). The Cognistat (Mueller, Kierman, & Langston, 2007) was developed by neurologists for bedside testing and also has a profile format that includes attention, language (naming, comprehension), calculations, and reasoning (similarities and judgment). It is used extensively by occupational therapists. Studies in a variety of client populations in which brain dysfunction is suspected show that the Cognistat is sensitive in detecting cognitive impairments, differentiating between groups, as

TABLE 3.2. Cognitive Screening and Baseline Status Tests •• •• •• •• •• ••

Mini-Mental State Evaluation (MMSE; Folstein & Folstein, 1975) Short Blessed test (Katzman et al., 1983) Clock Drawing Test (Freedman et al., 1994; Royall et al., 1998) Allen Cognitive Levels–5 (ACLS-5; Allen et al., 2007; www.allen-cognitive-network.org) Cognistat (Mueller et al., 2007; www.cognistat.com) Loewenstein Occupational Therapy Cognitive Assessment (LOTCA; Izkovich et al., 2000; www.maddak.org) Loewenstein Occupational Therapy Cognitive Assessment—Geriatric (LOTCA-G; Elazar et al., 1996; www.maddak.org) •• Dynamic Occupational Therapy Cognitive Assessment for Children (DOTCA-Ch; Katz, Parush, et al., 2005; www.maddak.org)

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well as measuring changes over time (Katz, Elazar, & Itzkovich, 1996; Katz, Hartman-Maeir, Weiss, & Armon, 1997; Lezak et al., 2004; Logue, Tupler, D’amico, & Schmitt, 1993; Osmon, Smet, Winegarden, & Gandhavadi, 1992). It was also found that the instrument is among the 20 most frequently used to evaluate cognitive baseline (Rabin, Barr, & Burton, 2005). The Loewenstein Occupational Therapy Cognitive Assessment (LOTCA), LOTCA Geriatric Version (LOTCA-G), and the Dynamic Occupational Therapy Cognitive Assessment for Children (DOTCA-Ch) are standardized instruments that have been studied in various populations (Elazar, Itzkovich, & Katz, 1996; Itzkovich, Averbuch, Elazar, & Katz, 2000; Katz, Parush, & Traub Bar-Ilan, 2005). They were developed by occupational therapists to assess cognitive skills that underlie everyday functioning in the areas of orientation, visual and spatial perception, praxis, visuomotor construction, thinking operations and memory; all together, 25 subtests in 5 areas. Both adult and elderly versions were studied extensively, and reliability as well as validity were determined in various populations (healthy adults and elderly patients following stroke and traumatic brain injuries [TBI]) and countries (Averbuch & Katz, 2005; Bar-Haim Erez & Katz, 2003; Cermak et al., 1995; Katz, Itzkovich, Averbuch, & Elazar, 1989; Katz, Kizony, & Parush, 2002). The DOTCA for children has data on typical children ages 6–12 years as well as data differentiating significantly between typical children and children with learning disabilities and TBI (Katz, Goldstand, Traub Bar-Ilan, & Parush, 2007; Ziviani et al., 2004). The aim of these assessments is to identify the cognitive skills necessary for occupational performance. As such, the specific deficit areas clarify clients’ difficulties in task performance and point to particular strategies that can be incorporated into treatment planning. At this stage of the process the occupational therapist should have a good idea about the clients’ level of self-­awareness, their previous and current occupational performance and participation, as well as their cognitive strengths and limitations. Although screening tools may have reduced sensitivity to subtle impairments, when paired with performance-based assessment, they do give a clinical indication of problems that require attention if the person is having difficulty performing tasks.

Measures of Cognition in Task Performance Six instruments are included as examples in this category. Two standardized tests stem from the cognitive disabilities model (Allen et al., 1992): the Cognitive Performance Test (CPT) and the Routine Task Inventory—­Extended (RTI-E). They include a battery of activities of daily living (ADLs) and instrumental activities of daily living (IADLs) (the CPT), including six tasks of medication use, shopping, preparing toast, using the phone, choosing appropriate dressing for an outing, and travel (Bar-Yosef, Weinblatt, & Katz, 1999; Burns, 2006; Levy & Burns, 2005). As well as a rating scale based on observations of everyday functioning that include 30 tasks in 4 areas—ADLs, IADLs, communication, and work readiness, the RTI-E includes descriptions of 6 levels for each task, and the rater chooses the level that best describes the current performance (Allen, 1989; Katz, 2006). The RTI-E can be conducted by the caregiver, the therapist, or the individuals themselves if they are able. The CPT and RTI-E follow the hierarchy of cognitive levels developed by Allen. The reliability and validity of these two tests have been extensively researched in psychiatric and

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dementia populations (Allen et al., 1992; Allen & Blue, 1998; Katz, 2006; Levy & Burns, 2005). The Kitchen Task Assessment (KTA; Baum & Edwards, 1993) is a standardized performance-based assessment of cognition and executive function. The investigator records the level of support needed to perform a simple cooking task (making pudding). Support levels include verbal cueing, need for physical assistance, or an indication that the person is not capable of performing the task. The individual is scored on his or her ability to initiate the task, execute the task (including organization, sequencing, judgment, and safety), and complete the task. The KTA serves three purposes: to determine which executive functions are causing performance problems (initiation, organization, sequencing, judgment, and/or completion); to determine an individual’s capacity for independent functioning; and to determine the level of assistance needed to complete the task (Baum & Edwards, 1993). Caregivers can then adjust the level of support they provide accordingly. The Kettle Test (Hartman-Maeir, Armon, & Katz, 2005) is a brief performancebased test that involves a task of assembling an electric tea kettle and preparing two different hot beverages. Following completion of the task, the therapist engages the client in a debriefing that focuses on the client’s evaluation of the performance. Task selection is designed to require basic cognitive abilities such as attention, perception, praxis, and memory, as well as requiring higher-order executive functions by providing unusual, novel, and complex conditions (the kettle is empty and disconnected; target ingredients are situated within distracters). Initial results with an older adult group (N = 41) showed significant (moderate to high) correlations with ADL and IADL measures as well as with tests of cognition. More recently Hartman-Maeir, Harel, and Katz (in press) found high interrater reliability between two sets of raters of 20 clients in stroke rehabilitation. Stroke survivors (N = 36) at discharge from rehabilitation were found to require significantly more assistance on the Kettle Test than matched controls, and their scores on the Kettle Test were moderately but significantly correlated with conventional cognitive and functional outcome measures. The Observed Tasks of Daily Living—Revised (OTDL-R) is a performancebased test that requires problem solving in IADL tasks (Diehl et al., 2005). It includes nine tasks in three areas: medication use, telephone use and financial management. The test discriminates between groups with cognitive impairments and those that are healthy. Categorization and deductive reasoning measures predict performance on the OTDL-R (Goverover & Hinojosa, 2002; Goverover & Josman, 2004). The Assessment of Motor and Process Scale (AMPS; Fisher, 2006a, 2006b) is based on the model of human occupation (Kielhofner, 2002) and was developed to assess the subsystem supporting an individual’s performance. In the context of this chapter the Process scale of the instrument is of interest as it measures integrated cognitive functions. The instrument includes a list of about 50 ADLs, mostly IADL tasks, from which the client and therapist choose two to three tasks that are familiar to the person and then the therapist observes as the person performs them. Such items may be making a sandwich, washing dishes, or paying a bill. The scoring yields both a motor and a process score and also identifies four levels of independence. The instrument was developed using a Rasch model (an item-­response model that transfers the total score to a linear score to create a value that can be used in analysis more readily than the raw total score, which has floor and ceiling effects) and applied to

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ASSESSMENT CONCEPTS AND METHODS TABLE 3.3.  Measures of Cognition in Task Performance •• Routine Task Inventory (RTI-E; Allen, 1989; www.allen-cognitive-network.org; Katz, 2006) •• Cognitive Performance Test (CPT; Burns, 2002, 2006) •• Kitchen Task Assessment (KTA; Baum & Edwards, 1993) •• Kettle Test (Hartman-Maeir et al., 2005, in press) •• Revised Observed Tasks of Daily Living (OTDL-R; Diehl et al., 2005) •• Assessment of Motor and Process Scale (AMPS; Fisher, 2006a, 2006b)

large and diverse populations (Fisher, 1993, 2006a, 2006b; Fisher, Liu, Velozo, & Pan, 1992; Kizony & Katz, 2002). In general, after the third stage of the evaluation process it is determined if more in-depth cognitive testing is necessary. When severe deficits are detected in the screening/baseline process, the cognitive testing can be stopped, unless specific deficits such as unilateral spatial neglect, attention, memory, or executive functions require further testing to guide interventions (see Table 3.3).

In-Depth Testing of Specific Cognitive Domains Under laboratory-like tests occupational therapists use those tests that were developed with the idea of simulating behavioral components of performance. The test batteries listed in Table 3.4—the Behavioral Inattention Test (BIT), Test of Everyday Attention (TEA), Rivermead Behavioural Memory Test (RBMT), and the Behavioural Assessment of Dysexecutive Syndrome (BADS)—all target specific domains in a variety of ways to assess the extent of the difficulties a client has in spatial neglect, attention, memory, and/or executive functions. Data from these tests enable therapists to form a more in-depth understanding of the problem a client has in performing daily routine tasks as well as complicated and novel occupations that may require multitasking abilities (Robertson, Ward, Ridgeway, & Nimmo-Smith, 1994; Wilson, Alderman, Burgess, Emslie, & Evans, 1996; Wilson, Cockburn, & Baddeley, 1985; Wilson, Evans, Emslie, Alderman, & Burgess, 1998). All four instruments are standardized and have been studied extensively with various populations. Attention and visuospatial neglect can also be tested by a new computerized program, the Visual Spatial Search Task (VISSTA; Bar-Haim Erez, Kizony, Shahar, &

TABLE 3.4. Cognitive Tests for Specific Domains •• •• •• •• •• ••

Behavioral Inattention Test (BIT; Wilson et al., 1987) Visual Spatial Search Task (VISSTA; Bar-Haim Erez et al., 2006) Test of Everyday Attention (TEA; Robertson et al., 1994) Rivermead Behavioural Memory Test (RBMT; Wilson et al., 1986) Behavioral Assessment of the Dysexecutive Syndrome (BADS; Wilson et al., 1996) Contextual Memory Test (Toglia, 1993) Toglia Categorization Test (Toglia, 1994) Both tests: Awareness of performance assessed pre- and posttesting.

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Katz, 2006), which was developed to enable precise measure of success rate and reaction time in a random and graded search task. The VISSTA shows significant construct validity in differentiating between groups of stroke patients and controls and good test–­retest reliability with standards for different age groups (Bar-Haim Erez, Katz, Ring, & Soroker, 2009). The program has different modules and is intended for training as well as assessment. Two additional instruments listed in this category are the Contextual Memory Test (CMT) and the Toglia Categorization Assessment (TCA) with Deductive Reasoning (DR); they were developed by Toglia in line with the dynamic model described above. The unique feature of these two tests is the dynamic component, whereby a graded cueing system provides a current level of ability in memory or categorization, as well as a level of ability when mediation is provided, namely, the potential ability. The discrepancy between the two scores, termed by Vygotsky (1978) the “zone of proximal development,” is the basis for much of the dynamic testing approach (Feuerstein, 1979; Grigorenko, & Sternberg, 1998; Sternberg & Grigorenko, 2002). In addition, these tests include an online evaluation of emergent awareness that enables the therapist to see whether clients’ level of awareness changed while they performed a task (Toglia & Kirk, 2000). Both tests were developed for clients with TBIs but were further studied in clients with schizophrenia as well as children with TBI and attention-­deficit/hyperactivity disorder (ADHD) (Goverover & Hinojosa, 2004; Josman, 2005; Josman, Berney, & Jarus, 2000a, 2000b). If further cognitive testing is required, particularly for mild cases, or when functional problems are reported but no deficits were found on initial measures, referral for extensive neuropsychological assessment is recommended and further exploration of the impact of the deficit on daily life must be determined. The tests described below may be useful in these cases.

Specific Cognitive Measures of Daily Functions At this point the specific cognitive area of deficit in daily functions has to be observed and measured in daily functions. The ADL Checklist for Neglect (Hartman-Maeir & Katz, 1995) and the Catherina Bergego Scale (Azouvi et al., 2003) both measure deficits in activities such as grooming, dressing, and eating, as well as reading, writing, and mobility. Namely, does the client neglect the left or right side of his or her personal or extrapersonal space without the knowledge that it occurs? This phenomenon is one of the most detrimental to rehabilitation of clients following stroke (Heilman, Watson, & Valenstein, 2003; Katz, Hartman-Maeir, Ring, & Soroker, 1999). Executive functions have been tested traditionally with neuropsychological measures such as the Wisconsin Card Sorting Test (WCST), Tower of London Test, and so on. However, it is now acknowledged by many researchers that to fully identify executive function deficits, tests in complicated, novel situations that require multitasking in daily activities should be conducted (Burgess et al., 2006; Katz & Hartman-Maeir, 2005; Stuss & Alexander, 2000) (see Table 3.5 on the next page). The Executive Function Performance Test (EFPT) was developed based on the KTA previously described (Baum, Morrison, Hahn, & Edwards, 2007; Baum et al., 2008) to record executive functions in the performance of a task. The EFPT includes four standardized IADL tasks (cooking, telephone use, medication management, and money management).

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ASSESSMENT CONCEPTS AND METHODS TABLE 3.5. Cognitive Measures in Daily Functions for Specific Domains •• Unilateral Neglect in ADL (Hartman-Maeir & Katz, 1995) or Catherina Bergero Scale (Azouvi et al., 2003) •• Executive Function Performance Test (EFPT; Baum et al., 2007) •• Multiple Errands Test (MET) hospital and simplified versions (Alderman et al., 2003; Knight et al., 2002) •• Pro-Ex Profile of Executive Control System (Braswell et al., 1993) •• Prigatano Competency Rating Scale (PCRS; Prigatano, 1986) •• Assessment of Awareness of Disabilities (AAD; Kottorp & Tham, 2005; Tham et al., 1999)

The therapist provides graded cues and determines a score for components of initiation, planning, execution of the task with error detection and correction, safety and judgment, and task completion. The EFPT has been validated in studies with older adults, with individuals with stroke, and with clients with schizophrenia (Baum et al., 2008; Katz, Felzen, Tadmor, & Hartman-Maeir, 2007). The Multiple Errands Test (MET) by Shallice and Burgess (1991) was further adapted and studied with clients with TBI (Alderman et al., 2003; Knight, Alderman, & Burgess, 2002) and those following stroke (Dawson et al., 2005; Rand, Weiss, & Katz, in press) and provides a more complicated multiple subgoal task performed in a shopping district. As Burgess and colleagues (2006) emphasize, the only accurate way to determine executive function abilities is to study them in natural environments with as much similarity as possible to the daily requirements of standards of performance in social community settings. In addition to the above performance measures the Pro-Ex (Braswell et al., 1993) is a 7-point rating scale that the therapist scores in the areas of initiation, planning, goal management, and awareness based on observations, interview with caregiver, or functional testing in daily occupational performance. Finally, evaluation of self-­awareness in daily activities is needed to understand how much clients can and may be willing to cooperate in their rehabilitation process, based on their level of awareness/unawareness. The Prigatano Competency Rating Scale (PCRS) questionnaire (Prigatano, 1986) is comprised of 30 daily activities or social encounters that the client and a proxy (e.g., relative, therapist) complete. The discrepancy between the two scores indicates overestimation (client rates self higher than the proxy) and suggests that the client is unaware of his or her daily performance and social behavior. The instrument was studied extensively and found to be reliable and valid (Hoofien & Sharoni, 2006; Katz, Fleming, Keren, Lightbody, & HartmanMaeir, 2002; Prigatano, 1999, 2005; Prigatano, Borgaro, Baker, & Wethe, 2005). The Assessment of Awareness of Disabilities (AAD; Kottorp & Tham, 2005; Tham, Bersnpang, & Fisher, 1999; Tham, Ginsburg, Fisher, & Tenger, 2001) is a guided interview with seven questions that measures the discrepancy between the client’s observed ADL ability on the AMPS and his or her perceived ability. Preliminary Rasch analysis suggests acceptable validity and affirms that the instrument measures one construct of awareness of disability. The AAD has demonstrated acceptable reliability (between raters and over time), internal consistency, and sensitivity to change (Tham et al., 1999, 2001).

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Evaluation of Environmental/Contextual Factors Occupational therapists understand that an individual’s abilities can be optimized by environments that support their ability to use their skills. Thus they closely consider the immediate natural environment of the person, especially the physical and human environment, focusing on resources and potential barriers to clients’ successful performance. For example, what are their living conditions? Are they safe? Do they live alone? Who can be “recruited” for assistance (family, friends, etc.). Many practitioners visit the client’s home to determine the safety of the physical environment. The environment must be assessed for people with cognitive loss to determine if they have the capacity to live alone safely. Three assessments that focus on home safety are discussed here: the Home Occupational Environmental Assessment (HOEA; Baum & Edwards, 1998), the Safety Assessment of Function and the Environment for Rehabilitation (SAFER tool; Chui et al., 2006), and the Home Environmental Assessment Protocol (HEAP; Gitlin et al., 2002, in Asher, 2007) (see Table 3.6). The HOEA is a checklist designed to identify how the home environment supports occupational performance and the safety of the person being assessed. It is particularly useful for clients with visual and cognitive impairments. It is completed by a therapist while in the client’s home and requires approximately 20 minutes. The HOEA checklist covers issues such as accessibility within the home, sanitation, food storage, safety issues, and lighting at the point of common tasks. The scoring indicates the independence of the person. The SAFER tool was designed to help therapists assess the client’s ability to safely carry out functional activities at home. It can be used with adults who have cognitive impairments, mental health problems, physical disabilities, and/or complex needs. It includes 97 items in 14 areas of concern, including mobility, kitchen use, fire hazards, wandering, and communication. Level of safety risk is rated on a 4-point scale, with higher scores indicating severer environmental problems. Internal consistency reliability and initial support for validity are reported (Asher, 2007). Most clinicians report that the SAFER tool is valuable because it provides a comprehensive assessment of safe functioning at home and useful ideas for environmental interventions. The HEAP is comprised of a caregiver interview and direct observation designed to assess the home environment of individuals with dementia and provide recommendations for home modifications. It includes 192 items in 8 areas of the house, such as bedroom, kitchen, and bathroom, which are scored for presence/absence of safety hazards, adaptations, visual cues, and comfort. High interrater agreement was found, and preliminary studies support content and convergent validity (Gitlin et al., 2002).

TABLE 3.6.  Environmental Assessments •• Home Occupational Environmental Assessment (HOEA; Baum & Edwards, 1998) •• Safety Assessment of Function and the Environment for Rehabilitation (SAFER; Chui et al., 2006). •• Home Environmental Assessment Protocol (HEAP; Asher, 2007; Gitlin et al., 2002)

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Summary An important skill in the training of occupational therapists is the capacity to do activity/occupational analysis. A task is evaluated for its cognitive, motor, sensory, psychological, and physiological demands. Occupational therapists are knowledgeable in the analysis of routine activities, taking into account the typical way the activity is performed, with special attention to cultural differences in the performance of tasks (Pierce, 2001). Utensil use is just one example, with some cultures eating with their hands, others with the fork in the right and knife in the left hand. There are many differences in how self-care, work, and community activities are valued and placed into routines. In all cases the occupational therapist lets the client define the goals that will be the basis of treatment. Few systems of analysis have been developed in occupational therapy that focus on the cognitive demands of desired/target occupations. The most established one is the activity analysis within the cognitive disabilities model developed by Allen (1985), which corresponds to the hierarchy of cognitive levels (Allen et al., 1992). Levy and Burns (2005) extended the analysis using the dimensions of attentional processes, working memory processes, and occupational behavioral responses at each of the six cognitive levels, with rehabilitation potential predicted from this analysis. In the multicontext treatment approach (Toglia, 2003, 2005), a system of subgoaling, according to areas of concern, is developed that outlines strengths within skill areas, subskills, and strategies that need to be strengthened, first using simulated tasks agreed upon with the client. Thus activities are chosen with the client and awareness strategies, techniques, and processing strategies are learned to accomplish desired performance of the activities (Toglia, 2005). Occupational therapists use the person’s capacities and the affordances offered by the environment to foster his or her occupational performance. These professionals work with people in hospitals, rehabilitation hospitals, in the home, and in the work environment to help people gain the skills that will support their recovery and learn strategies to manage any residual cognitive impairments. The Cognitive Functional Evaluation (CFE) provides a description of cognitive strengths and weaknesses and their implications for occupational performance. It further provides recommendations concerning the type and amount of assistance currently required for safe and meaningful occupational performance, and it provides the basis for clinical reasoning in selecting a cognitive model for intervention and a treatment approach. The factors that enter into the decision-­making process follow the three perspectives of a PEO model: •• Person: severity of cognitive deficits and variance of cognitive profile (areas of strengths and deficits), learning potential (declarative and procedural memory capacities), awareness of deficits and disabilities, psychological factors, disease/injury variables (time postonset, severity, progressive, etc.) •• Environment: safety of the environment; human, physical, economical, and/or cultural resources or barriers to rehabilitation •• Occupation: previous and current activities that can be used in the intervention to sustain and support independence, health, sense of self and identity, social interaction, and meaningful activities.

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Recent Intervention Studies by Occupational Therapists Intervention studies are essential to provide evidence-based practice and constitute a future direction for the field of occupational therapy. Recent controlled group intervention studies have been conducted to investigate the effectiveness of a range of intervention methods, with different populations and at different stages of disabilities, that focus on cognitive domains and aim to increase participation in daily life. Several studies are summarized to highlight the strategies that are being employed. Bar-Haim Erez (2006) studied the effectiveness of phasic alerting (PA) treatment (Robertson, Mattingley, Rorden, & Driver, 1998) combined with visuospatial search training (using the VISSTA program; Bar-Haim Erez et al., 2009) for patients with right-­hemisphere strokes and unilateral spatial neglect (USN) at the postacute stage. Eighteen patients were randomly assigned to three groups (experimental—PA + spatial training; only spatial training; no spatial training). All patients were tested with an extensive assessment battery (including paper-and-­pencil and functional tests) at three points in time (pre-, posttreatment at 2–3 weeks following 10 sessions, and follow-up after 5 additional weeks). Results show a significant advantage of the experimental group receiving PA treatment with spatial search task. On average the experimental group showed a recovery trend of significant improvement immediately posttreatment that was maintained at the follow-up stage. The other two groups improved to a lesser degree, and this improvement was detected mostly at the followup stage after an additional 5 weeks, not immediately after the treatment stage. The relative improvement of the experimental group was apparent in USN measures as well as in functional measures such as the ADL checklist (Azouvi et al., 2003), room description (Frassinneti, Angeli, Meneghello, Avanzi, & Ladavas, 2002) and general daily functions (Functional Independence Measure [FIM]; Granger, 1993). The findings suggest that the use of alerting methods combined with visuospatial training in the acute stage poststroke is beneficial and has effects on USN itself as well as functional implications. Katz, Ring, and colleagues (2005) aimed to determine whether nonimmersive interactive virtual environments are an effective medium for training individuals who suffer from USN as a result of a right-­hemisphere stroke, and to compare it to a standard computer visual scanning training method within a rehabilitation program. Participants included 19 patients with right-­hemisphere stroke randomly assigned to two groups: 11 in an experimental group were given computer desktop-based virtual reality (VR) street-­crossing training, and 8 in a control group were given computer-based visual scanning tasks, both for a total of 12 sessions, 9 hours total, over 4 weeks. Measures included USN assessments, paper-and-­pencil tests, and an ADL checklist; a test on the VR street program; and actual street crossing videotaped. Testing was performed pre- and postintervention. Results showed that, on the USN measures, the VR group achieved results that equaled those achieved by the control group treated with conventional visual scanning tasks. However, the VR group improved more on the VR test and did better on some measures of the real street crossing. Despite some inequality in the group assignment, the findings support the effectiveness of the VR street program in the treatment of stroke patients with USN, and suggest further development of VR programs with ecological significance (Weiss, Kizony, Fientuch, & Katz, 2006).

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Rand, Abu-Rukun, Weiss, and Katz (2009) studied the validity of an intervention using VR of a supermarket (VMall) as an assessment tool for executive functions, followed by the study of its effectiveness with postacute stroke survivors for improving their shopping in a real mall. An additional question of the study related to whether the intervention would improve executive functioning (Rand et al., 2009). Seven stroke survivors living at home 5–27 months postevent participated in the study. A series of single-­subject studies using an A-B-A design was conducted. Intervention included 10 sessions over 3 weeks, and testing took place at four points in time (baseline, pre- and postintervention, and 2-week follow-up). Patients were assessed with executive function measures, including the Zoo Map from the BADS (Wilson, Alderman, Burgess, Emslie, & Evans, 1996) and the MET (Knight et al., 2002) as well as a virtual version of the MET, the VMET. The VMET was performed in a virtual large supermarket VMall programmed as an application within GestureTek’s Gesture Xtreme (GX) video-­capture VR system (Rand, Katz, Shahar, Kizony, & Weiss, 2005; Weiss, Rand, Katz, & Kizony, 2004). Participants see their performance on the screen, thus receiving immediate feedback while manipulating the environment with the upper extremity. The performance of the shopping task provides multiple opportunities to make decisions, plan strategies, and multitask. Results show that the number of mistakes made while performing the MET in both the real shopping mall and the VMall decreased following the intervention. Percent improvement on all MET measures was substantial (26–50%). All patients returned to shopping in a real supermarket on their own or with caregivers and expressed high satisfaction and enjoyment from the intervention. Findings suggest that executive functions as well as IADL performance can be improved using VR intervention (Rand et al., in press). In another study that focused on executive function deficits, Keren (2006) studied the effectiveness of the goal management treatment (GMT) method (Levine et al., 2000) developed as an occupational goal intervention (OGI) with a population of clients with schizophrenia. The OGI uses a five-stage process (stop, define, list, learn, check) in a variety of graded individualized occupational tasks (Keren & Katz, 2005). Eighteen subjects—six in each of three groups—­comprising two experimental groups (OGI and frontal-­executive program [FEP]; Delahunty & Morice, 1996) and a control group participated. Evaluations included measures of executive functions (BADS, EFPT), cognition in task performance (RTI-E), and participation (Activity Card Sort [ACS], Return to Normal Living [RNL]). Clients were assessed at three points, pre- (T0), posttreatment (T1; treatment lasted 6 weeks and included 18 onehour sessions), and at 6-month follow-up (T2). Results show that participants in the study groups (OGI and FEP) improved significantly in executive function measures at T1 in comparison to the control group. A significant improvement was found in the OGI group on most measures of activity and participation outcomes at T1, whereas less significant improvements were found in the other groups. The majority of the participants’ achievements were maintained when tested at T2. The results support the effectiveness of the OGI for patients with schizophrenia in improving both executive functioning and daily activities and participation. However, further studies are needed to validate the findings. Finally, Hartman-Maeir, Eliad, and colleagues (2007) studied the effectiveness of a community intervention program for functional status, leisure activity, and sat-

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isfaction of adult first-­stroke survivors, and compared these outcomes with those of stroke survivors not attending any program (Hartman-Maeir, Soroker, Ring, Avni, & Katz, 2007). Participants were living at home at least 1 year post onset. Twentyseven were participants in a community rehabilitation program and 56 were nonparticipants. Outcome measures included the Stroke Impact Scale (SIS; Duncan et al., 1999), FIM, IADL, activity card sort, and the Life-­Satisfaction–9 questionnaire (LiSat-9; Fugl-Meyer, Branholm, & Fugl-Meyer, 1991). Results revealed severe stroke impact and low functioning in ADLs/IADLs in the participant group, but the level of participation in leisure activities improved significantly after attending the program. The comparison with the nonparticipant group revealed that participants were significantly more disabled in ADLs/IADLs than nonparticipants (this outcome was expected because some of the participants were in a chronic state and still needed rehabilitation). Despite their disability status, the satisfaction rates of participants were significantly higher than nonparticipants from “life as a whole” and from their leisure situation. Findings suggest that stroke survivors participating in a communitybased rehabilitation program did not show an advantage in terms of disability levels over nonparticipants. However, their activity level increased, presumably due to the program, and their satisfaction scores were higher than those of nonparticipants. In summary, the issues faced by individuals with neurological dysfunctions demand interdisciplinary work to link brain function with behavior, performance, and everyday life. The science is evolving to inform interventions. The next step is to continue to determine the effectiveness of interventions and to disseminate the strategies to clinicians who work with people with neurological dysfunctions and to families who are central to supporting the recovery and adaptation process.

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S e c tio n B

Assessment of Specific Functional Abilities and Assessment Considerations

Chapter 4

The Relationship between Instrumental Activities of Daily Living and Neuropsychological Performance David Loewenstein and Amarilis Acevedo

I

n order for individuals to live independently, they must have the ability to take care of themselves and to function autonomously in their environment. Difficulties with independent function as a result of cerebral impairment have profound effects on both the physical and psychological well-being of the patient and his or her family. Functional impairment also has significant financial consequences for individuals as well as for society as a whole. The ability to accurately assess both higher- and lowerorder functional abilities is critical to the remediation and management of persons with brain-­related impairments. Functional abilities are typically divided into two groups: basic activities of daily living (BADLs) and instrumental activities of daily living (IADLs). BADLs are those tasks that are related to basic self-care, such as feeding, dressing, self-­transfer, toileting, and grooming (see Kane & Kane, 1981; Katz, Ford, Moskowitz, Jackson, & Jaffe, 1963). On the other hand, IADLs refer to activities of daily living that, as the name implies, are instrumental in allowing the individual to effectively interact with the environment to obtain needed goods and services. IADLs have higher cognitive complexity than BADLs, which are more rudimentary in nature. IADLs are required for independent living at home and within the community and allow the individual to cope with the demands of everyday life. IADLs include, but are not limited to, shopping, taking medications, cooking and performing other household chores, managing money and personal finances, and using means of communication (e.g., telephone, mail) and transportation (e.g., driving, taking the bus or subway) (Kane & Kane, 1981; Lawton & Brody, 1969; Tuokko, 1993). IADLs are generally distinguished from specific vocational or work-­related skills that are necessary for gainful employment. Although IADLs typically refer to activities in the home environment, IADL impairment in areas such as utilizing transportation or using different means of communication may adversely impact upon work attendance and/or performance.

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The interest in the assessment of the ability to perform activities of daily living has its origins in rehabilitation medicine and occupational therapy (Bennet, 2001; Loewenstein & Mogosky, 1999). The primary goal of the functional assessment of activities of daily living is to identify the patient’s strengths and weaknesses so that these are incorporated in treatment planning and in rehabilitation and management efforts. Depending on the expertise of the health professional and the clinical setting, he or she may be asked to render an opinion regarding the degree to which an individual is able to independently and safely carry out BADLs or IADLs. For example, physicians who specialize in physical medicine and rehabilitation, and occupational and physical therapists who work in rehabilitation institutions with individuals with severe head injury or advanced neurological diseases or dementias, are frequently asked to determine the degree to which BADLs may be compromised in a given individual. In contrast, neuropsychologists working in private practice, in outpatient settings with mildly impaired brain injury, or other nonrehabilitation settings are more likely to be asked to render an opinion about an individual’s cognitive status and its impact on his or her ability to drive, manage finances, self-­administer medications, and carry out other IADLs. The opinions of these professionals could lead to changes in the home environment to protect the individual’s safety, and could have a profound impact on the individual’s autonomy, including decisions about guardianship and living arrangements. In addition to patient care, the assessment of an individual’s ability to carry out IADLs is an essential part of diagnostic procedures that require the identification of functional deficits thought to be present in various psychiatric and neurodegenerative disorders. As an example, the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American Psychiatric Association, 1994) requires that deterioration in social and/or occupational functioning be present for a diagnosis of most major psychiatric conditions or dementia. In fact, one of the primary differences between an individual who meets criteria for dementia, according to the National Institute for Neurological and Communicative Diseases and Stroke—­A lzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA; McKhann et al., 1984) and an individual with mild cognitive impairment (MCI) is that the former requires the presence of functional impairment whereas the latter requires relatively intact functional status in the presence of cognitive deficits. The discussion that follows examines the ability to perform IADLs from a cognitive standpoint. In our view there are three key elements that need to be considered when assessing the impact of cognitive status on the ability of individuals to perform IADLs: the elements of causation, change, and specificity. The element of causation refers to the fact that the loss of the ability to perform an IADL needs to be cognitive in nature and not secondary to physical limitations. As an example, a right-­handed patient may be unable to write checks because of the loss of control of the right arm after a left-­hemisphere stroke. If the patient is conceptually able to describe all the steps that would be needed to complete the task correctly, including what information needs to be included in the check and where it should be placed, then there has been no cognitive-­functional loss in his or her ability to write a check. This point underscores the importance of conducting a thorough medical evaluation to rule out physical factors (e.g., poststroke paresis, loss of vision secondary to diabetes retinopathy) as the main reason for impairments in the performance of an IADL. In cases

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where physical and cognitive factors coexist in a given individual, it is usually difficult to disentangle the relative contribution of each factor in the resulting functional deficit. For example, a young adult who lost motor control of his arm as a result of a traumatic brain injury and an older adult who has limited vision may both have memory problems superimposed upon their physical limitations. Clearly, the interdisciplinary collaboration of physicians, neuropsychologists, occupational therapists, and physical therapists is needed for the comprehensive evaluation and treatment planning of such cases. The element of change refers to the need to compare an individual’s current ability to perform an IADL with his or her ability and to carry out the task in the past. For example, if a patient was never able to balance a checkbook or had never engaged in balancing a checkbook and is still unable to do the task, then there has been no functional loss in that ability. In other words, the inability of a person to perform a functional task that he or she has never performed or mastered may not constitute actual functional decline. In our work with older adults from heterogeneous backgrounds, we find that many of our patients have never performed banking transactions electronically or by using automated menu-­driven telephone systems. Thus, the fact that they may not be able to perform this task at present does not constitute change and should not be conceptualized as evidence of cognitive-­functional decline. The requirement that functional deterioration be present to meet DSM-IV criteria for dementia assumes that the clinician has adequate understanding of a person’s premorbid functioning and that deterioration in function parallels decline in cognitive abilities rather than merely reflecting lack of familiarity with a task. In addition, health professionals must be aware that there are instances in which a person who never learned to drive, prepare meals, or manage finances may be suddenly jeopardized by the loss of a significant other who once performed those activities. In these cases, these skills were never learned but may remain important targets for assessment and intervention. The element of specificity can be divided into three types that frequently coincide: task-, person-, and environment-­specificity. Different IADLs have different cognitive characteristics and demands. Thus, an individual may be unable to perform a specific IADL but may still be able to perform other IADLs without difficulty. For example, individuals who have amnesic MCI (see Petersen et al., 1999) are more likely to have difficulty with IADLs that have strong episodic memory demands (e.g., remembering to take medications) than with those with more procedural motor demands (e.g., dialing a telephone number). Some tasks have multiple cognitive determinants, any one of which can adversely affect functional performance. For example, paying bills is an important component of the ability to manage finances. However, a component analysis of this task reveals that an individual might not be able to pay a bill after a brain injury because of the inability to understand the bill or because he or she has forgotten that the bill arrived. Provided that the person has intact prospective memory (i.e., the ability to remember to perform an intended action), which would allow him or her to remember the intended action of paying the bill, the person may still be unable to pay the bill because of confusion as to how to write a check or prepare a letter for mailing. There are also instances in which the individual manages to pay a bill but because of difficulties in balancing his or her checkbook, the checking account may have insufficient funds. All of these possible causes for not paying the bill may

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potentially lead to deleterious consequences (i.e., losing the electricity in one’s home, discontinuation of telephone services). Thus, it is not only important to understand functional deficits in terms of task-­specific performance but also by gaining an appreciation for specific elements that underlie a particular functional deficit. Task-­specific factors interact with person-­specific variables such as cognitive and functional reserve. Given a similar pattern and degree of cerebral dysfunction, an individual who may have worked as an accountant may evidence better performance on functional tasks related to finances relative to someone who recently had to learn this skill because of the death of a spouse. Other person-­specific variables may include the individual’s ethnocultural/linguistic background, premorbid strengths and weaknesses, compensatory abilities and motivation, amount of practice with certain IADLs, and degree to which a task has been overlearned. In fact, many caregivers of patients with dementia become aware of the patient’s functional changes when they observe the difficulties faced by their loved ones when trying to perform IADLs in new circumstances or unfamiliar environments (e.g., preparing a meal in an unfamiliar kitchen). Preparation of a meal requires an interaction between taskand subject-­specific characteristics; difficulties encountered in an unfamiliar kitchen reflect environment-­specific characteristics. Persons with brain injury or cognitive impairment typically fare better when performing routinized tasks in familiar environments and in the presence of overlearned situational cues.

Assessment of IADLs To assess an individual’s ability to perform IADLs, clinicians typically utilize information from one or more of the following sources: (1) self-­report by the individual, (2) information provided by the individual’s informant(s) (e.g., relatives, close friends, proxy), or (3) direct observation of the individual’s ability to perform tasks that are similar to the functional task in question. Each of these methods of assessment has inherent strengths and weaknesses (see Loewenstein & Mogosky, 1999).

Self-­Report and Informant Report Information about the patient is usually provided by the patient him- or herself and/or by informants. Both the self-­report by the individual and the report by the informant(s) are usually elicited in a clinical interview, which may be supplemented by information obtained from questionnaires and/or rating scales. Although most clinicians seem to agree about the importance of asking the patient about his or her functional status, there are conflicting reports in the literature regarding the extent to which self-­reports of functional status should be considered valid. Myers, Holliday, Harvey, and Hutchinson (1993) reported a high correspondence between the self-­report of older adults regarding their functional abilities and their actual performance in the home. In contrast, other investigators have shown that, relative to younger adults, old and very old individuals may be less accurate in their judgment of their own functional capacities (Hoeymans, Wouters, Feskens, van den Bos, & Kromhout, 1997; Sinoff & Ore, 1997). Other studies have questioned the relative weight that should be given to the report of patients them-

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selves versus their informants. Various investigations have shown that patients with dementia often overestimate their functional abilities, whereas caregivers may either overestimate or underestimate these abilities (Argüelles, Loewenstein, Eisdorfer, & Argüelles, 2001; Loewenstein et al., 2001; Mangone et al., 1993; Weinberger et al., 1992). While neurologically impaired patients commonly underestimate their deficits because of agnosia, it is also possible that those with significantly depressed mood may complain more about their inability to carry out daily functional activities. Furthermore, depressed caregivers may be particularly susceptible to overreporting functional impairment. There are a plethora of self-­report measures and of informant-based rating scales for assessing patients’ ability to perform BADLs and IADLs (Lindeboom, Vermeulen, Holman, & De Haan, 2003). The reader is referred to Loewenstein and Mogosky (1999) for a description of the strengths and weaknesses of many available measures. Perhaps the most widely used method of ascertaining a patient’s ability to perform IADLs is via informant report by a relative or close friend who has had the opportunity to observe the patient in his or her real-world environment. One of the first measures created using this approach was the Instrumental Activities of Daily Living Scale (IADLs) by Lawton and Brody (1969). This measure taps abilities to engage in activities such as shopping, managing finances, taking medications, preparing food, doing laundry, and using the telephone. Other commonly used IADL scales administered to the patient and/or the informant include the Bayer Activities of Daily Living Scale (BADLS; Erzigkeit et al., 2001), the Disability Assessment for Dementia (DAD; Gelinas, Gauthier, McIntyre, & Gauthier, 1999), the Older Adults Resource Center Scale (OARS; Fillenbaum & Smyer, 1981), and the Functional Activities Questionnaire (FAQ; Pfeffer, Kurosaki, Harrah, Chance, & Filos, 1982). One of the advantages of utilizing self-­report scales is the ease of administration and scoring and the fact that they can be filled out by the patient and/or informant while in the waiting room. A major disadvantage of self-­report measures when evaluating neurologically impaired patients such as those with Alzheimer’s disease or specific right-­hemisphere cerebral infarctions is that they may exhibit varying degrees of anosognosia. Given that these patients may be unaware of their deficits and changes in their functional abilities, self-­report measures may overestimate their actual functional status. Another disadvantage of self-­report methods is that even individuals who are aware of their deficits may choose not to report changes in their functional status for fear of social stigma and/or losing their independence (e.g., especially driving privileges). An advantage of informant-based scales is that the informant usually rates the patient based on real-world functional performance of IADLs. Thus, informantbased scales tend to be less susceptible to those fluctuations in the patient’s cognitive status and motivation that may affect the performance and behavior in the clinician’s office. In addition, given that the informant is likely to interact with the patient over long periods of time and in many situations, his or her report may serve as an overall estimate of the individual’s functional status across settings and time. Specifically, information provided by a knowledgeable informant is helpful to the clinician who is attempting to establish the degree to which an individual has evidenced functional deterioration relative to his or her premorbid level of function. On the other hand, an informant may feel uncomfortable reporting changes in the patient’s functional

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ability out of a sense of loyalty or for fear that the family member might lose critical privileges, including driving. The level of stress and depression, marital dynamics, and individual personality styles that involve minimization and denial as well as overexaggeration may further serve to affect the accuracy of the self-­report of the spouse and other family members.

Performance-Based Approach The performance-based approach usually requires the patient to perform the particular activity under the observation of the examiner, who utilizes behaviorally based measures to assess different aspects of functional capacity. Performance-based assessments have the advantage of providing an objective behavioral evaluation of functional skills that are required for daily living, such as using the telephone, meal preparation, medication management, writing a check, balancing a checkbook, and making change for a purchase. Such direct assessment is particularly useful in the evaluation of patients who may not have an informant, in cases where there is doubt about the validity of information provided by a sole informant, and in cases where there are discrepant opinions among multiple informants. Direct observation of the individual as he or she carries out the functional tasks can be conducted in the clinic and/or the patient’s home. Various research groups have developed standardized testing protocols so that the assessment of an individual’s functional capacity can be objectively assessed and quantified. Such tests have typically been developed for older adults and those with cognitive impairment and include some of the measurements described in further detail below.

Performance Test of Activities of Daily Living The Performance Test of Activities of Daily Living (PADL; Kuriansky, Gurland, & Fleiss, 1976) categorizes patients into one of three levels of functional independence (independent, moderately dependent, or dependent) through the administration of 16 tasks related to basic and independent activities of daily living (IADLs). Most tasks include the manipulation of props, which can be assembled into a portable kit and administered in remote settings. The PADL includes the assessment of grooming, hygiene, eating, dressing, communication, time orientation (e.g., telling time on a clock), and safety awareness (e.g., turning a light switch on and off). The PADL was designed so that tasks could be easily understood and carried out by the patient. Task instructions are simple and direct, and facilitate translation of the instrument for use in other languages. The props help convey, nonverbally, what is expected. A trained paraprofessional can administer the PADL in about 20 minutes.

Direct Assessment of Functional Status Scale The Direct Assessment of Functional Status (DAFS; Loewenstein et al., 1989) scale was originally developed to assess functioning in Alzheimer’s disease and related disorders, but researchers have also found it useful with other patient populations, such as those with schizophrenia (Evans et al., 2003; Patterson et al., 1998) and Hunting-

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ton’s disease (Hamilton, 2000). The test measures functional ability across multiple tasks in both BADL and IADL domains, including time orientation, communication, transportation, financial skills, shopping, grooming, and eating. Two unique features of the DAFS include a memory task in the shopping subscale (recall and recognition of a grocery list) and the optional transportation subscale, which assesses an individual’s ability to understand and respond to road signs. Examining functional capacity on the transportation subscale is important for patients who are still driving but for whom driving competence may be a concern. The DAFS has also been translated for use in non-­English-­speaking populations and takes 30–35 minutes to administer.

Structured Assessment of Independent Living Scales The Structured Assessment of Independent Living scales (SAILS; Mahurin, De Bettignes, & Pirozzolo, 1991) divides 50 ADL tasks into 10 subscales, including fine motor, gross motor, dressing, eating, expressive language, receptive language, time orientation, money-­related skills, instrumental activities, and social interaction (e.g., appropriate responses to social greetings). In addition to the total score, reflective of overall functioning, the SAILS generates a motor score and a cognitive score. Administration and scoring of the SAILS is guided by detailed, behaviorally anchored descriptions and can be used in both clinical and research settings. Overall, the tasks take approximately 60 minutes to complete.

Assessment of Motor and Process Skills Designed as a tool for occupational therapists, the Assessment of Motor and Process Skills (AMPS; Fisher, Leu, Velozo, & Pan, 1992) measures the quality of performance by the effort, efficiency, safety, and level of independence involved in both ADL motor and process skills. Motor skills include actions in which the client moves him- or herself or an object, whereas process skills are actions involving a logical sequence of steps, appropriate tool/material selection, and adaptation to problems as they occur. The AMPS inventory contains 83 standardized ADL tasks, varying in degree of difficulty, and a brief interview is used to help the client choose two tasks that are particularly relevant and familiar. For performance comparisons, the AMPS computer package adjusts scores for task/item difficulty and rater severity. Training to administer and score the AMPS includes a 5-day workshop plus follow-up reliability and tester calibration requirements; however, the test has been standardized cross-­culturally and internationally (e.g., the United States, England, Sweden, Japan) on over 100,000 individuals for use in research and clinical practice. Administration typically takes about 45 minutes, but the time required varies by tasks chosen and individual level of functioning.

Cognitive Performance Test The Cognitive Performance Test (CPT; Burns, Mortimer, & Mechak, 1994) is comprised of six common daily tasks, including dressing, shopping, toast making, telephone use, washing, and traveling. The assessment focuses on the degree to which an individual’s functional abilities and deficits affect performance on these tasks; simple

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task completion is not the main variable of interest. Performance is classified into one of six ordinal levels of functional disability that range from profoundly disabled to normal functioning (these levels are based on Allan’s [1982] cognitive disability theory). During testing, as deficits or competencies appear, the tester changes the task demands according to a standardized procedure, thus tailoring the test for each individual throughout the administration. The degree and type of help required for task completion are then reflected in the rating. For example, an individual whose task performance is organized, efficient, and without error would be rated as a level 6 (normal), whereas a participant who shows a trial-and-error approach and often needs additional specific directions to complete the task is functioning at level 4 (moderate functional decline). Completion of the battery takes approximately 45 minutes.

Kitchen Task Assessment The Kitchen Task Assessment (KTA; Baum & Edwards, 1993) focuses solely on the ADL task of cooking, and analyzes performance in terms of the cognitive processes involved and the subsequent level of cognitive support needed to complete the task. During the KTA, which can be administered in the clinic or the home, the individual is asked to make cooked pudding. The tester evaluates the performance across multiple components, and scoring is based on whether each component was completed independently, with verbal assistance, with physical assistance, or not completed at all. From task observation and the scored results, the tester or clinician can recommend appropriate strategies that caregivers can use to help the impaired individual complete other ADLs. The KTA takes less than 30 minutes to administer and is appropriate for use in both clinical and research settings.

Test of Everyday Functional Ability The Test of Everyday Functional Ability (TEFA; Weiner, Gehrmann, Hynan, Saine, & Cullum, 2006), which was originally called the Texas Functional Living Scale, was designed as a brief measure of functional competence. This 21-item test includes subscales related to dressing, time, money, instrumental activities (e.g., addressing an envelope, using a telephone), and memory (e.g., remembering to take medications). The TEFA can be administered in about 15 minutes by a bachelor’s-level tester. Functional assessments have also been developed for use in other populations. The UCSD Performance-Based Skills Assessment (UPSA; Patterson, Goldman, McKibbin, Hughs, & Jeste, 2001) was designed for persons with schizophrenia or schizoaffective disorder and was developed to tap skills in areas such as communication, finance, household chores, transportation, and recreational activities. The ability to manage medications in individuals with schizophrenia can also be assessed employing instruments such as the Medication Management Ability assessment (MMA; Patterson et al., 2002). Heaton and colleagues (2004) have described the use of performancebased tasks that tap financial skills, medication management, shopping, and cooking as well as specific vocationally related skills in individuals with HIV infection. Despite the abovementioned strengths, the performance-based approach has its limitations. Performance on tests of functional capacity may not always capture

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patient-­specific or environment-­specific variables that affect real-life performance and that, although present in the testing situation, may not be present in the patient’s everyday environment. Some of these variables involve the ability to self-­initiate and complete a task, the overall motivation of the patient, and the presence of environmental variables that cue the patient that the task needs to be performed. Moreover, the same task that can be completed in the laboratory under optimal conditions may not be as successfully performed in an environment with multiple cognitive and task demands or with less structure. An illustration of this principle is often seen in acquired brain injury. In a quiet office, a secretary recovering from a brain injury may have the cognitive, motivational, and functional capacity to use the telephone, take a message, and type. However, if placed in a busy office in which attention has to be divided among various distractors (e.g., patients presenting at a reception area, the ringing of the telephone, a letter that is being typed), the individual’s functional performance may be severely compromised.

Clinician-Based Evaluations It has been increasingly recognized that good clinical judgment may depend on data garnered from a wide variety of sources. For example, physicians, occupational therapists, and physical therapists in outpatient rehabilitation settings may rely on their direct observations of performance-based behaviors, staff ratings of the patient’s functional ability, and judgments of family members who have a chance to evaluate the patient in his or her real-world environment. In some instances, a wealth of functional information may be derived from home visits conducted by a nurse, occupational therapist, physical therapist, or a social worker. An advantage of this interdisciplinary approach is that the effects of physical limitations, cognitive limitations, and motivational factors can be weighed when arriving at a diagnostic determination and a comprehensive treatment plan.

The Role of Neuropsychological Assessment in the Assessment of Ability to Perform IADLs From its earliest days, clinical neuropsychology aspired to understand the impact of brain lesions and diseases on cognitive functioning. Early work with individuals who suffered penetrating brain injury, blunt head trauma, or strokes culminated in a rich understanding of the relationship between the damaged brain structures and the multiple facets of memory and of other cognitive processes (e.g., attention, executive function). Our knowledge of the cognitive sequelae of brain injury continues to be enriched by advances in psychometrics, neuroimaging, and cognitive neuroscience. A long-held assumption in the field of neuropsychology is that cognitive processes involved in memory, language, visuospatial skills, attention, and executive function underlie most IADLs. A logical conclusion of such an assumption is that the measurement of cognitive status should allow the clinician to infer the functional status of the patient (see Loewenstein & Mogosky, 1999). Certainly, it is likely that those with substantial cognitive impairment will have difficulties on many higherorder functional tasks, particularly those that involve multistep cognitive operations or divided attention. It is difficult to imagine an individual with profound generalized

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neuropsychological impairment managing his or her finances, driving an automobile, or returning to the many functional demands of everyday life. In actual clinical practice, however, persons frequently have only mild or moderate cognitive impairments in specific domains, with some areas evidencing only minimal or no cognitive deficits. Varying strengths and weaknesses and differences in cognitive reserve among individuals (see Scarmeas & Stern, 2004; Whalley, Deary, Appleton, & Starr, 2004) may act as mediating factors between actual brain injury or disease and the individual’s ability to function. In our work, we have encountered persons with brain injuries who have significant cognitive impairment but who nevertheless continue to show relatively preserved functional abilities. This suggests that, in addition to cognitive reserve, individuals may also vary in their functional reserve. As previously discussed, the ability to perform IADLs is likely related to a combination of person-, task-, and environmental-­specific factors. This complexity may explain in part why knowledge of neuropsychological function alone may not provide sufficient information in many cases to make judgments about the person’s ability to perform IADLs in real-world settings. In general, the literature across different patient groups suggests that there is an association between neuropsychological test performance and the ability to perform IADLs. Neuropsychological function, most notably executive ability, has been shown to relate to functional competence in diverse groups such as community-­dwelling older adults (Bell-McGinty, Podell, Franzen, Baird, & Williams 2002; Cahn-­Weiner, Boyle, & Malloy, 2002; Cahn-­Weiner, Malloy, Boyle, Marran, & Salloway, 2000; Rapp et al., 2005; Royall, Palmer, Chiodo, & Polk, 2005) and in patient populations diagnosed with Alzheimer’s disease (Boyle, Paul, Moser, & Cohen, 2004; Cahn-­Weiner, Ready, & Malloy, 2003), cerebrovascular disease (Jefferson, Paul, Ozonoff, & Cohen, 2006), postacute head injury (Farmer & Eakman, 1995; Goverover, 2004), heart transplantation (Putzke, Williams, Daniel, Bourge, & Boll, 2000), schizophrenia (Jeste, Patterson, et al., 2003), and HIV infection (Albert et al., 2003; Heaton et al., 2004). Early cognitive deficits have been related to an increased risk of functional decline among older adults (McGuire, Ford, & Ajani, 2006) and to increased mortality (McGuire et al., 2006; Schupf et al., 2005). Early functional deficits have also been related to cognitive decline in longitudinal studies of older adults (Plehn, Marcopulos, & McLain, 2004). In addition, there are specific patterns of neuropsychological deficits that may be related to functional performance. Earnst and colleagues (2001) found that performance on neuropsychological tests tapping the executive component of working memory was strongly associated with performance on a test of functional capacity that assessed basic money skills and ability to manage bank statements and a checkbook. In a study of 69 older patients who presented for clinical assessment, Baird, Podell, Lovell, and McGinty (2001) found that in addition to the Dementia Rating Scale, seven out of nine neuropsychological measures entered into regression equations predicting scores on a scale that assesses the ability to carry out IADLs. In a recent investigation, Hoskin, Jackson, and Crowe (2005) found that neuropsychological performance was related to the capacity of persons with acquired brain injury to manage their personal finances. These investigators compared participants who were handling money independently with those who had been appointed an administrator by the court to help them manage their finances. Results indicated that measures of working memory, impulse control, and cognitive flexibility correctly

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classified 83.7% of individuals in the correct functional group. Interestingly, measures of memory had no discriminatory power. Woods and colleagues (2006) found that the ability to retrieve words that refer to action (i.e., verbs) was more strongly associated with IADL dependence among HIV-infected individuals, relative to the ability to retrieve words that start with a specific letter or that belong to a particular category, resulting in an overall hit rate of 76%. In addition, performance on neuropsychological measures such as memory, attention, and conceptual abilities has been related to medication adherence and management (Hinkin et al., 2002; Jeste, Dunn, et al., 2003; Putzke et al., 2000). Cognitive performance has also been associated with performance on driving simulators and on-road driving evaluations (Grace et al., 2005; Lundqvist et al., 1997; Marcotte et al., 2004; Reger et al., 2004; Rizzo, McGehee, Dawson, & Anderson, 2001). A particularly effective predictor of driving performance has been the Useful Field of View (UFOV), a test tapping visual attention (see Clay et al., 2005). A primary goal of neuropsychological assessment is to determine patterns of cognitive strengths and weaknesses as they relate to important real-world outcomes (Sbordone, 1996). In their review of the literature, Franzen and Wilhelm (1996) and Spooner and Pachana (2006) differentiate between veridicality and verisimilitude in describing the ecological validity of neuropsychological tests. “Veridicality” refers to the extent to which performance on neuropsychological tests relates to measured performance on real-world tasks, whereas “verisimilitude” refers to the degree that the task demands of a test reflect the actual demands imposed on the person by the real-world environment. Clearly, most studies in the field are concerned with veridicality. Increasingly, there has been recognition that neuropsychological measures administered in controlled conditions that facilitate optimal performance may not tap the real-world demands of higher-order functional tasks that often must be completed in the presence of many environmental demands. In our laboratory we have worked on paradigms designed to increase verisimilitude. For example, in our studies of MCI in older adults, we have been developing and refining paradigms that tap time- and event-­related prospective memory as well as face–name associations and memory for common, everyday objects. Thus, we have focused on paradigms that more closely tap some of the real-world difficulties reported by subjects with MCI. Although traditional tests of auditory list learning, memory for story passages, and memory for designs are often useful as cognitive tests, their applicability to real-life demands (e.g., remembering to take medications at a specific time, putting a name together with a face) may be more limited. We first developed the DAFS (see above) to assess the real-world abilities of persons with mild dementia. More recently, and in conjunction with Dr. Sara Czaja and her human factors team at the University of Miami, we have developed assessment and outcome measures such as the ability to navigate telephone menu systems and to use an automated teller machine (ATM) (Loewenstein & Acevedo, 2006).

Limitations of Neuropsychological Studies Based on Correlation Analyses At face value, the above-mentioned studies indicate that there is a significant association between neuropsychological test performance and the ability to carry out IADLs. However, one must be cautious about applying group findings to individual

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cases and about making causal inferences on data that assess statistical associations. Although most studies demonstrate statistically significant relationships between neuropsychological measures and functional performance, the degree of variability in neuropsychological performance and functional performance frequently does not exceed the unexplained variance associated with the dependent variable (see Loewenstein & Mogosky, 1999; Silver, 2000). Specifically, even if the association between the variables of interest exceeds a healthy correlation of .7, more than 50% of the performance variability on functional measures remains unexplained. In our judgment, more informative methods to determine the utility of neuropsychological measures in predicting actual functional performance include techniques such as logistic regression, discriminant function analysis, and receiver operating characteristic (ROC) curve analysis. These approaches yield estimates of sensitivity and specificity, providing information to the clinician about how many persons with functional impairment are accurately identified as impaired and how many persons without functional impairment are accurately identified as unimpaired by neuropsychological tests. The next step would be to calculate positive and negative predictive values based on the base rates of impairment in specific settings. Unfortunately, there is a paucity of such studies in the literature. Many clinicians would feel comfortable concluding that an individual with a normal neuropsychological profile is likely to be able to drive and to manage his or her medications or finances. On the other hand, it is difficult to imagine that a clinician would feel comfortable recommending these activities for an individual scoring below the 1st percentile on a broad array of commonly employed neuropsychological measures of memory, language, attention, executive function, and visuospatial skills. The patients who fall in the mild-to-­intermediate impairment ranges in neuropsychological test performance are the ones who often constitute a challenge when trying to make judgments about their degree of functional impairment. While there is little debate that specific cognitive abilities underlie functional capacity, it should be recognized that neuropsychological measures are not unidimensional but rather tap multiple cognitive functions. More importantly, given that functional performance in real life is often dependent on the complex interaction of person-, task-, and environmental-­specific variables, it is not surprising that there is far from a one-to-one correspondence between neuropsychological test results and IADLs. Indeed, in our work on cognitive remediation techniques for those with early Alzheimer’s disease, we pay special attention to task specificity. We have found that the use of spaced retrieval (see Camp & Stevens, 1990) and procedural motor memory practice can lead to improvements in performance on functionally relevant tasks in individuals with mild Alzheimer’s disease. The concept of spaced retrieval is based on paradigms that require the individual to make associations between two targets (e.g., face–name association) and to gradually lengthen the interval between the presentation of the target and recall of the association by the patient. If the individual fails at a longer interval, the interventionist returns to the last previous shorter interval in which there was success. Procedural learning involves more implicit motor memory subserved by basal ganglia systems and is not as dependent on explicit memory, which is very dependent on the integrity of hippocampal and entorhinal cortex structures. In contrast, we have found that simply training different component cognitive processes (e.g., attention, concentration) thought to underlie task performance

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has no effect on outcome (Loewenstein & Acevedo, 2006; Loewenstein, Acevedo, Czaja, & Duara, 2004).

Minimizing Errors of Clinical Judgment In general, functional assessment is most complete when information is obtained from multiple sources. It is important to gather as much information as possible from collateral informants regarding the patient’s current and past ability to perform specific IADLs. In addition, an examination of the patient’s performance on neuropsychological measures may be helpful when making treatment recommendations and when deciding if referrals or additional assessments (preferably in home) are necessary. For example, an individual, his or her spouse, and their children may insist that the patient is able to drive independently, manage finances, and buy needed goods. However, on direct functional assessment, the patient is unable to count currency, make change for a purchase, write a check, or balance a checkbook. Neuropsychological testing may also evidence severe impairments in memory, attention, visuospatial skills, concept formation, speed of processing, and in the ability to shift cognitive sets. Despite the reports of the patient and family members, it is likely that the patient is at risk. In many states across the country, the results of such an evaluation would prompt a report to the state of concerns about the patient’s driving ability and a recommendation that an on-road driving test be conducted. The issue is not merely whether the person has the procedural knowledge and motor skills to operate a vehicle but whether he or she has the cognitive capacity to recognize changing environmental conditions, such as watching out for children in a school zone or taking an alternate route if a road is blocked. Additionally, it might be necessary for a nurse or a social worker to perform a home visit to ensure that the person is actually capable of managing his or her finances and medications in the everyday environment. The nature of the neurological status of the patient as well as the possible need of serial assessments should also be taken into consideration. For example, an individual with a head injury may have a functional disability that dissipates over time and that will show improvement in subsequent evaluations. Conversely, a person with early Alzheimer’s disease may not demonstrate functional impairments at a given time but may evidence these deficits on follow-up assessments.

Future Directions It has been argued that the development of neuropsychological tasks that tap the demands of everyday life (i.e., verisimilitude) should be an important goal for future test development in neuropsychology (Chaytor & Schmitter-­Edgecombe, 2003; Spooner & Pachana, 2006). Spooner and Pachana (2006) mention the possible applicability of tests such as the original Rivermead Behavioral Memory Test (RBMT; Wilson, Cockburn, & Baddeley, 1985) and an extended version of the test (RMBT-E; de Wall, Wilson, & Baddeley, 1994; Wilson, Clare, Baddeley, Watson, & Tate, 1999) in the identification of everyday memory impairments, especially those related to prospective memory deficits. Indeed, they endorse the further development of neuropsy-

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chological tests that can be administered to neurologically intact as well as medically or neurologically impaired patients. There is a great need for the development of more sensitive and ecologically valid neuropsychological measures that would allow us to further understand the effects of medications and medical conditions on cognitive test performance, especially among older adults. Emerging technologies using computer microprocessors may enable the development of more sophisticated performance-based measures to assess attention, cognitive processing speed, and working memory, and to examine the relationship of these cognitive processes to functional test performance. In this regard, human factors engineers have made significant contributions to the field by studying how to optimize human–­machine interfaces that are used in everyday appliances and systems such as cell phones, telephone menu navigation systems, and even the automobiles that we drive (Czaja & Sharit, 2003). It is increasingly evident that new empirically based approaches are needed to more fully capture the demands of real-world activities of daily living. In addition, there is increasing emphasis on assessing the practical everyday implications of theoretical research that studies constructs such as memory and executive function. Neuropsychologists, occupational therapists, and other allied health professionals have been at the forefront in developing performance-based instruments for older adults. There is also an increasing appreciation of the value of developing new paradigms that integrate information from related fields of cognitive psychology, human factors, and behavioral medicine. In fact, the challenge to neuropsychology is to appreciate the richness of alternative approaches developed by allied disciplines and to find ways of incorporating these approaches in our continuing pursuit of scientific knowledge. Researchers and clinicians recognize that those neuropsychological tests that may be useful for diagnosis may not necessarily be the same measures that are most useful for monitoring cognitive and functional change over time. Similarly, the neuropsychological tests that may be useful for diagnostic purposes may not be the optimal measures to predict real-world functional performance. Real-life situations are usually based on open systems where environmental circumstances are fluid and may be unpredictable. In contrast, strict standardization procedures require the administration of cognitive measures in a controlled testing environment that minimizes distractions and maximizes test performance. A continuing challenge to the field is to develop standardized instruments that adequately capture the multiple demands that are placed simultaneously on the individual’s cognitive resources. The limitations of available cognitive tests in the prediction of functional performance in the real world are sometimes not appreciated by neuropsychologists who may be asked to render an opinion about the patient’s functional status. The professional opinion rendered by a neuropsychologist may be used by physicians, rehabilitation treatment teams, the schools, and the courts to make decisions that may dramatically affect the patient’s quality of life, autonomy, and independence. For example, a cognitively normal, older, non-­native, English-­speaking immigrant with 6 years of education, whose sole work experience has been repetitive manual labor, may score at the impaired level on neuropsychological tests frequently used by neuropsychologists, such as the Rey–­Osterrieth Figure Test, the Boston Naming Test, the Trail Making Test, and subtests of the Wechsler Adult Intelligence Scale (e.g., Similarities, Block Design). If the neuropsychologist conceptualizes the test results as

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a true reflection of the patient’s cognitive status without consideration of the limitations of many mainstream neuropsychological tests when used with individuals of different ethnocultural/linguistic backgrounds, he or she may erroneously conclude that the patient’s “cognitive impairment” is likely to result in inability to carry out IADLs. In fact, several cross-­cultural studies examining the functional status of older adults have had to rely on IADLs that differ from those traditionally assessed in scales used in the United States (see Fillenbaum et al., 1999; Senanarong et al., 2003). In other words, IADLs vary across cultures and those that may be essential in certain cultural groups may be irrelevant in others. On the other hand, the neuropsychologist may be evaluating a patient who scores within normal limits on memory for stories on the Wechsler Memory Scales and who exhibits normal expressive and receptive language function on the Boston Diagnostic Aphasia Examination. Unfortunately, normal performance on these measures does not guarantee that the patient will be able to manage and process the welter of discourse material that individuals must manage in their everyday environment. Similarly, normal performance on a list-­learning task does not necessarily imply that the person will remember to buy needed grocery items, to pay the electricity bill, or to appropriately respond to environmental cues that signal that the bill should be paid or that it was already paid. The expertise of neuropsychologists in test development and construction places neuropsychology in a unique position to develop tests with verisimilitude. In addition, it allows our field to advance our knowledge of factors, including ethnocultural/linguistic factors, that mediate the relationship between cognition and real-life functioning.

Conclusions It is increasingly recognized that measures that presumably tap specific cognitive processes rarely tap a unitary cognitive construct and that, rather, performance on widely employed instruments frequently tap various cognitive domains. The notion that cognition, as reflected by neuropsychological tests, is the sole requisite for independent real-world function is, at best, misguided. It is important that we further our knowledge of person-, task-, and environment-­specific variables that may affect real-world functioning. Pioneering work by Wilson and colleagues (Wilson, 1993; Wilson et al., 1999) and the cogent arguments presented by Spooner and Pachana (2006) underscore the importance of examining the practical aspects of memory (e.g., prospective memory) that are rarely assessed in traditional neuropsychological measures, thus limiting their ecological validity. This laudable goal would be facilitated by an integration of information stemming from allied disciplines such as rehabilitation medicine, occupational therapy, human factors engineering, and behavioral neurology. Already, psychologists in rehabilitation settings are developing sophisticated treatment approaches that go beyond a specific cognitive domain and that directly train the acquisition and maintenance of functional skills (see Loewenstein & Acevedo, 2006). To enhance clinical utility, future studies should assess the impact of varied neurological conditions on specific IADLs. In addition, empirical studies should utilize techniques such as ROC curves, logistic regression, and discriminant function analysis

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to examine outcomes of interest (e.g., sensitivity, specificity, positive and negative predictive values) in different clinical populations and in groups of varied ethnocultural/ linguistic backgrounds. The identification of factors other than neuropsychological test performance that can augment the prediction of ability to carry out IADLs in real life would further advance our knowledge in this important field. The complexity that neuropsychologists face in understanding the multifactoral nature of functional performance on different IADLs can appear daunting. The alternative, however, is to refuse to accept the limitations associated with existing practices and the consequences of making inaccurate judgments that can adversely affect the lives of our patients and the fulfillment of our professional obligations.

Acknowledgment We would like to thank Rachel Meyer for her assistance in reviewing and summarizing the available measures of functional capacity.

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Chapter 5

The Prediction of Vocational Functioning from Neuropsychological Performance Joseph R. Sadek and Wilfred G. van Gorp

D

espite the fact that a range of neuroimaging procedures has greatly reduced the need for neuropsychological tests to determine presence and lesion location, neuropsychological tests continue to have an important place in the clinical assessment of brain dysfunction and cognitive capabilities, relative weaknesses and frank deficits. Relating findings on neuropsychological tests to “real-world functioning” represents one of the most valued uses, today, of neuropsychological assessment. However, the current array of neuropsychological tests was not designed to predict real-world abilities in any but the broadest manner (such as an IQ score predicting general ability to function, overall). Unfortunately, neuropsychological tests do not correlate perfectly with functional outcomes, including vocational outcomes in persons with acquired brain disease. One way to characterize the problem (Franzen & Wilhelm, 1996) is that neuropsychological tests have limited “veridicality,” which is the ability to accurately predict real-world outcomes such as future employment or job performance, and that they lack “verisimilitude,” which is the similarity to real-world tasks (including work skills). Although we do not have, as yet, an array of neuropsychological tests that are known to directly predict specific aspects of real-world functioning, the tests we use today still have a very important role in assessing outcomes. Neuropsychological tests remain the most direct way to assess cognitive and emotional abilities that are important for vocational and academic performance. However, the challenge remains to develop and validate a new generation of neuropsychological tests that relate to both brain function as well as predict some aspects of specific real-world abilities. This chapter discusses these important issues. Conditions that affect a person’s cognitive functioning can have a profound impact on his or her ability to work. Although acquired or traumatic brain injury (TBI) is the most widely studied condition, with annual estimated costs in lost productivity and medical care around $60 billion (Finkelstein, Corso, & Miller, 2006), other conditions that result in cognitive impairment (e.g., stroke, brain tumors, mul-



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tiple sclerosis, HIV/AIDS) have also been shown to result in lost productivity relating to work and other outcomes. Some psychiatric disorders (e.g., posttraumatic stress disorder, bipolar disorder, major depression, and schizophrenia, among others) are also associated with cognitive impairment and resulting loss of work productivity. In most of these disorders, there is little research on the relative contribution of affective symptoms versus cognitive impairment to loss of work productivity. In schizophrenia we know that neuropsychological impairment is an independent predictor of unemployment (Heaton et al., 1994; McGurk & Mueser, 2004, 2006; Twamley et al., 2006). Most treatment approaches aim to maximize a person’s functional independence, including return to work, and there is a clear need for research on cognitive factors that predict return to work in any condition associated with neuropsychological impairment. It is, of course, unrealistic to expect that every important behavior relevant to performance in any job for each person undergoing a neuropsychological assessment can be assessed by one or more neuropsychological tests. Reviews of the cognitive aptitudes required for occupations are listed in the Dictionary of Occupational Titles (DOT—Lees-Haley, 1990; U.S. Department of Labor, 1991). In the updated DOT, called the Occupational Information Network (O*NET; U.S. Department of Labor, 2006), there are 21 possible “cognitive abilities” listed, an additional 10 “psychomotor abilities” and 12 “sensory abilities” that are considered “worker characteristics”— enduring characteristics that may influence both performance and the capacity to acquire knowledge and skills required for effective work performance (see Table 5.1 for O*NET worker abilities). Many of the abilities are ones that neuropsychologists have also traditionally measured (e.g., auditory attention and finger dexterity). It is important to note that the abilities considered important for various job titles were determined by expert panels and not by empirical validation using actual tests. A brief electronic review of the Mental Measurements Yearbook (MMY; Buros Institute of Mental Measurement, 2005) using the keyword vocation indicates that at least 100 performance-based assessment tools have been published. These assessments may maximize verisimilitude, but it is unlikely that, even if valid, an individual professional could learn to administer and interpret performance on every job-­specific performance assessment tool. Therefore, a different approach is required. One perspective on neuropsychological tests in relation to real-world functioning is that the tests measure meta-­abilities that are generally applicable to performance of a broad range of vocations (a global “g” [or general] factor, if you will, of “real-world ability”) rather than predicting discrete or specific vocations. A closer inspection of the various descriptions of the vocation-­specific measures listed in the MMY reveals that each can be classified into one of two groups. One group contains measures that assess a job-­specific skill, such as specific clerical or mechanical skills. The other group contains measures that assess a broader cognitive ability that can be applied in many vocations, such as verbal, computational, or visuospatial abilities. Of the 100 vocation-­specific tests we identified, almost half (53) assess some aspect of cognition such as vocabulary, problem solving, or verbal comprehension. Although there are no studies that directly compare the predictive validity of neuropsychological versus vocation-­specific assessments, it seems reasonable to expect that neuropsychological tests perform as well as many of these cognitively themed vocational tests in assessing vocational performance, since they both measure

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TABLE 5.1.  Abilities—Enduring Attributes of the Individual That Influence Performance Cognitive abilities

Psychomotor abilities

Sensory abilities

Verbal abilities Oral comprehension Written comprehension Oral expression Written expression

Fine manipulative abilities Arm–hand steadiness Manual dexterity Finger dexterity

Visual abilities Near vision Far vision Visual color discrimination Night vision Depth perception Glare sensitivity

Idea generation and reasoning abilities Fluency of ideas Originality Problem sensitivity Deductive reasoning Inductive reasoning Information ordering Category flexibility Quantitative abilities Mathematical reasoning Number facility Memory Memorization Perceptual abilities Speed of closure Flexibility of closure Perceptual speed Spatial abilities Spatial orientation Visualization Attentiveness Selective attention Time sharing

Control movement abilities Control precision Peripheral vision Multilimb coordination Response orientation Rate control Reaction time and speed abilities Reaction time Wrist–finger speed Speed of limb movement

Auditory and speech abilities Hearing sensitivity Auditory attention Sound localization Speech recognition Speech clarity

Physical abilities Physical strength abilities Static strength Explosive strength Dynamic strength Trunk strength Endurance Stamina Flexibility, balance, and coordination Extent flexibility Dynamic flexibility Gross body coordination Gross body equilibrium

Note. From U.S. Department of Labor, National O*NET Consortium. O*NET OnLine (interactive web application). Available at online.onetcenter.org.

the similar construct of cognitive abilities. If this expectation proves true, neuropsychological tests may be a useful alternative to the administration of job-­specific tests, especially in circumstances in which the issue of brain dysfunction exists. The neuropsychological test can, therefore, assess both the effect of central nervous system dysfunction as well as at least some aspects of real-world capability. This chapter reviews recent data on the ability of neuropsychological tests to predict both the ability to resume working and the quality of work performance after an acquired brain dysfunction. Here we emphasize the ability of neuropsychological tests to predict vocational performance in the context of specific illnesses or conditions. If, for example, future job performance were best predicted in a TBI population by the presence and duration of posttraumatic amnesia, and neuropsychological tests added little or no predictive power beyond this factor, there would be limited utility and rationale for then administering neuropsychological tests if we were most interested in job performance. Of course, when the details of the TBI are unknown, some sort of testing may be the only basis for predicting future vocational performance. As we review here, studies that control for disease severity demonstrate that

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neuropsychological tests do have predictive validity above and beyond disease characteristics. And because neuropsychological studies of vocational outcomes have not been performed in every disease population, it is necessary to extrapolate findings from the few populations that have been studied (e.g., TBI, HIV/AIDS) to populations that have not been studied as extensively (e.g., multiple sclerosis, neurotoxic substance exposure). In this chapter we present a model of the relationship between neuropsychological tests, brain dysfunction, and vocational outcomes, and we review studies of the ability of neuropsychological tests to predict employment outcomes. We also review recent literature on specific neuropsychological abilities, such as executive function and memory, to predict vocational outcomes, and we briefly review performance-based assessment of work skills. We conclude with recommendations for future directions for the development and validation of a new generation of neuropsychological tests to relate to vocational outcomes. Other disciplines, such as occupational therapy and human factors research, have objectives related to vocational functioning, but from a slightly different perspective. In general, occupational therapy focuses on treatment of disabled populations to maximize functional outcomes, including vocational functioning (Greig, Nicholls, Bryson, & Bell, 2004; Tsang, 2003). As described below in the theoretical models section, occupational therapy is more broadly focused on all factors that impact employment outcome, in addition to cognition, whereas neuropsychology is focused solely on cognition. Human factors is an interdisciplinary field devoted to the advancement and implementation of knowledge about human characteristics as they relate to the design of systems and devices. There is emphasis in this field on both theoretical model development (Gonzalez, 2005; Gonzalez, Thomas, & Vanyukov, 2005) and on changing systems and devices to adapt to human characteristics (Wilson, 2006). We let the other chapters in this volume address issues from their respective positions, but it is likely that the same problems regarding the best way to measure outcome are faced by all who are involved in this line of research.

Measurement of Vocational Outcome Vocational outcomes can be measured in a variety of ways. Most studies employ outcome as a dichotomous variable: a patient either resumes employment (or other productive societal role) or does not. Recently, however, there has been an emerging emphasis on assessing the quality of employment as one measure of outcome as well. Other studies have measured the stability of employment outcomes. For example, employment status may be measured at two different time points. In Nybo, Sainio, and Muller’s (2004) study, patients with TBI sustained injury as children between 1959 and 1969, were assessed again in 1985, and for the most recent assessment were seen again in 2001. The authors still used a dichotomous variable in that everyone was classified as either employed or unemployed, but they were able to compare employment outcomes approximately 40 years postinjury to those measured approximately 24 years postinjury. Twenty of the 27 participants had no change in employment status compared to the prior evaluation, and of the remaining seven cases, four transitioned from full-time or subsidized work to not working, whereas three transitioned to full-time work.

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Employment can be measured in terms of quality. One group (Klonoff, Lamb, & Henderson, 2001) assessed outcomes of their rehabilitation program by characterizing their participants’ 11-year outcomes as productive or nonproductive, with “productive” defined as full-time or part-time work or school, volunteering, or homemaking; “nonproductive” was defined as retired or not working. They also characterized their sample as either full-time paid work or not. Some studies characterize outcomes as return to work either at premorbid levels or at a “modified” (i.e., reduced) level (e.g., Ruffolo, Friedland, Dawson, Colantonio, & Lindsay, 1999). An interesting and unique approach to vocational outcomes controls for unemployment rates within the region where patients live (Doctor et al., 2005). These authors devised a predicted employment rate based on current employment statistics and compared a brain-­injury unemployment rate versus a predicted rate that took into account demographic factors. As might be expected, they found that the unemployment rate for the brain-­injured group far exceeded that predicted by each participant’s demographic profile. One of the most detailed assessments of vocational stability was conducted by Machamer and colleagues (Machamer, Temkin, Fraser, Doctor, & Dikmen, 2005), who assessed job stability 3–5 years post-TBI and defined job stability in several ways, including number of months worked full time, number of full-time jobs, and duration of uninterrupted full-time work. This study represents one of the most sophisticated assessments of job stability in the neuropsychological literature and is an example of the kinds of advances that should be made in studies that assess vocational outcome. From a rehabilitation perspective, Machamer and colleagues’ (2005) approach allows for the measurement of key elements of job performance that are related to self­sufficiency, the maximization of which is the goal of rehabilitation. The only missing variable is whether the person can sustain financial independence, but this is understandably difficult to measure. Measurement of vocational outcomes should always control for non-­neuropsychological confounding factors that might affect motivation to return to work, especially the existence of litigation to obtain compensation for the injury or the presence of disability benefits that might reduce motivation to return to work. Later in the chapter we review performance-based measures of employment functioning. Such direct measurement of job performance yields the most valid measures of employment outcomes and should become a focus of vocational outcome studies in the future.

Theoretical Underpinnings Regarding the Relationship between Cognitive Performance and Vocational Functioning The major models of functional outcome (including vocational outcome) in relation to brain dysfunction come from the TBI literature (Kendall & Terry, 1996; Ownsworth & McKenna, 2004). These models are comprehensive and rightly include all factors that impact outcome, of which cognitive abilities are just one. Other noncognitive contributors include premorbid factors such as preillness intellectual abilities, demographic factors, substance use history, premorbid employment history, available resources (e.g., socioeconomic status of the patient, social support), situational factors (e.g., status of the job market, the ability of a job to accommodate certain dis-

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abilities), and injury factors (e.g., physical impairment). Figure 5.1 depicts the model proposed by Kendall and Terry (1996). These authors devised this model because they believed that most studies and clinical decisions assumed that in TBI, neurological factors (e.g., characteristics of the injury, cognitive impairment) explained psychological adjustment and well-being. These authors reviewed the literature on non-­neurological factors that influenced adjustment post-TBI and found that many factors (e.g., preinjury factors) can contribute to outcomes. Though they do not define “psychosocial outcomes” or any of the other constructs in their model, the model nevertheless has had significant influence on researchers and rehabilitation specialists because it formalized the role of non-­neurological factors in determining outcome. Ownsworth and McKenna (2004) proposed a model that focused on rehabilitation after brain injury, in which they highlight intrapersonal factors such as self­awareness and other metacognitive and emotional factors (Figure 5.2) as they relate to successful rehabilitation (Ownsworth & McKenna, 2004). This model was developed based on a systematic review of the empirical literature. They characterized the

perceived stigma

Cognitive Impairment

lesion locus

Neurological Factors

perceived uncertainty

subjective severity primary appraisal

perceived control secondary appraisal

injury severity self-esteem

self-efficacy

Preinjury Functioning

Personal Resources

locus of control

Appraisal

social support family style

Environmental Resources

Psychosocial Adjustment

Coping

financial status

(multiple dimensions)

other stressors age at injury

Situation Factors

emotionfocused coping

problemfocused coping

other injuries physical disability

predictive relationships representative relationships

FIGURE 5.1.  Model of functional outcomes proposed by Kendall and Terry (1996). Reprinted with permission from Taylor & Francis, Ltd.

Early recovery

Pretrauma

Prediction of Vocational Functioning Preinjury variables (age, race, education, and occupation)

Injury variables (severity of TBI and functional status in the acute recovery phase) Neuropsychological variables (deficits in memory, attention, executive functioning, language, visuospatial skills, and processing)

Metacognitive and emotional variables (awareness, emotional well-being, motivation, and use of strategies)

Long-term adjustment

119

Social/environmental variables (Litigation, family and peer support, employer support, rehabilitation, and work experience)

u u u u u

Modify through rehabilitation Self-awareness training Group rehabilitation Training in compensation Motivational interviewing Adjustment counseling and cognitive-behavioral therapy

Modify social environments Provide more financial incentives for work u Employer education and training u Family education u Supported employment programs u Changes in public policy and funding decisions u

Employment outcome: type of work, number of hours, work modifications, quality of performance, and durability

FIGURE 5.2.  Model of employment outcomes proposed by Ownsworth and McKenna (2004). Reprinted with permission from Taylor & Francis, Ltd.

quality of studies included in their review and identified well-­defined injury and preinjury factors to assess the level of support the data suggest for each factor’s influence on outcomes. This model was designed specifically to assess vocational outcomes, in contrast to Kendall and Terry’s (1996) psychosocial outcomes model. Intrapersonal factors are among the targets of their rehabilitation approach, such as developing insight and compensation strategies. Other targets for rehabilitation are categorized as environmental factors, such as employer education and training, family education, and supported employment. Although the topic of rehabilitation is beyond the scope of this chapter, the distinction must be made between impairment and disability (Wilson, 2000). The World Health Organization (WHO; 2001) has adopted the International Classification of Functioning, Disability, and Health (ICF). The ICF represents a movement away from the concept of disability and a disease-­oriented concept of activities. For the purposes of our discussion, we retain the idea of disability since this chapter has a disease­oriented focus. It can be said that “impairment” is the deficit caused by physical or mental structures, whereas “disability” and “functioning” are the behavioral outcomes caused by impairment (Wilson, 2000). The concept of disability focuses on the behavioral deficits that can be observed, whereas functioning is a more neutral term that characterizes both intact and impaired abilities. The models proposed by

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Kendall and Ownsworth also focus on impairment and disability (Kendall & Terry, 1996; Ownsworth & McKenna, 2004). In this chapter we focus on the relationship between impairment and work disability. The model we utilize is a simplified version of previous models in which brain dysfunction results in cognitive, emotional, and behavioral impairment, which in turn results in specific disabilities and poor vocational functioning. Although the models developed by Kendall and Ownsworth were designed around TBI, we believe that the models are just as applicable to other etiologies of cognitive impairment. The existing models are neutral with regard to what course the disability will take, except that they assume that rehabilitation can change the outcome. This issue is important because the prediction of impairment is, of course, complex and multifactorial, taking into account such factors as premorbid characteristics, current resources, and emotional, behavioral, and environmental factors. As Sherer and colleagues (2002) concluded from their review, the timing of the neuropsychological evaluation relative to the onset of the disease can have an impact on the predictive utility of the neuropsychological test data. In the discussion below of Sherer’s review, we note that methodological concerns in this line of research limit any interpretation of the conceptual implications of early versus late neuropsychological assessment in TBI. Wilson and Watson (1996) proposed a model consisting solely of cognitive abilities based on Backman and Dixon’s (1992) theory of the development of compensatory behavior. Both models center on an acquired discrepancy between a person’s skills and environmental demands on those skills. In relation to brain disease, there is a decrease in skill caused by the disease or condition while the environmental demands remain either the same or increase. In this model “mechanisms” (e.g., treatments) are applied to create a match between environmental demands and skills. There is a distinction between normal compensation in an unimpaired person versus the extraordinary compensation that is used for the individual with brain damage. Such compensatory behaviors are considered extraordinary because the unimpaired person would not normally use them, such as the use of visual cues to focus attention in the patient with hemispatial neglect. The final component of the model is the consequence of the adaptive behavior, with the successful consequence being that which results either in the matching of the disabled skill level to the environmental demands and/or adequate performance of the desired behavior. We now turn our attention to a review of the research on neuropsychological impairment and its relationship to vocational functioning.

Sequential Model Because this chapter focuses on the role of cognitive performance in vocational functioning, we assume a model similar to that depicted in Figure 5.3, in which brain dysfunction causes cognitive impairment, and the cognitive impairment then causes impaired vocational functioning. We focus on studies of brain conditions that reduce a person’s previously intact vocational performance. The key assumptions of our model is that intact cognitive abilities are required for adequate work performance, and that brain disease directly causes cognitive impairment, which then directly causes vocational impairment.

Prediction of Vocational Functioning

Brain Disease

Cognitive, Emotional, Behavioral Impairment

121

Disability

Poor Vocational Functioning

FIGURE 5.3.  Hypothetical model with directional causal relationships among constructs.

In general, there are three trajectories of cognitive performance associated with acquired brain disease: (1) abrupt onset with gradual recovery to premorbid level, (2) abrupt onset with recovery to static impairment, and (3) gradual onset with progressive worsening over time. The first two courses are commonly seen after TBI, stroke, brain tumor, encephalitis/meningitis, and delirium, whereas the third course is typically seen in progressive dementia conditions or other degenerative neurological diseases. Most developmental disorders such as mental retardation, pervasive developmental disorders, learning disabilities, and attention-­deficit/hyperactivity disorder typify a static course with no change in abilities.

Controlling for Confounding Factors In order to draw sound conclusions about the predictive power of neuropsychological abilities, Sherer and colleagues (2002) provide a detailed method for rating studies on generalizability, reliability, and methodology in assessing employment outcome. They based their rating system on a more general rating system proposed by the Committee on Empirically Supported Practices of Division 40 (Division of Neuropsychology) of the American Psychological Association (Heaton, Barth, Crosson, Larrabee, & Reynolds, 2002). The committee’s recommendation contained five categories of study types, including studies that are strongly supportive, supportive, tentatively supportive, insufficiently or inconclusively, or studies that contraindicate a practice from being useful. Sherer and colleagues expanded this criteria set into two sets of criteria. One set focused on generalizability and reliability of the actual study, and the other set focused on the adequacy of the methodology. We highlight here the importance of controlling confounding factors in studies of vocational outcomes, especially predysfunction employment status such as poor preillness vocational attainment. In addition, other factors to consider are premorbid intellectual and neuropsychological abilities, psychiatric disorders, substance use disorders, and the age at which the disease or injury occurred. A special note should be made of the issue of age at disease onset. Some studies have noted that older age predicts worse employment outcomes. The issues surrounding age are complex and are not reviewed in detail here (see Hedge, Borman, & Lammlein, 2006, for a comprehensive review). They include the potential for older age to amplify the cognitive impact of brain disease, psychosocial issues such as age discrimination in the workplace, and the additional impact of an employer’s unwillingness to hire a person who suffered a neurological insult. It has also been reported in a long-term follow-up of persons who sustained a TBI that age at the time of injury uniquely predicted independent functioning 5–20 years after leaving a rehabilitation program (i.e., working, going to school full time, or living alone) (Wilson, 1992). Some studies have also hypothesized that persons who suffer brain disease at an

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advanced age may be less motivated to return to work, since they have fewer years of productive work remaining. Researchers have observed in a state-run vocational rehabilitation program that patients with brain injury who were older than the age of 44 had no worse employment outcomes than those who were injured at younger ages (Skeel, Bounds, Johnstone, Lloyd, & Harms, 2003). When it is considered as a continuous variable rather than a categorical one (Wood & Rutterford, 2006), age may not independently predict employment outcomes more that 10 years after TBI. Because of the divergence in findings regarding age of disease onset, more studies are needed to fully understand the impact of age on vocational outcomes in TBI and other brain diseases.

Review of Neuropsychological Studies and Vocational Outcomes In the following section we review studies in which neuropsychological tests were used to predict vocational outcome. Most studies use inclusion and exclusion criteria to control for many premorbid factors, such as premorbid IQ, learning disabilities, substance use, and so on, but most do not control for age at disease onset. One area that is rarely considered is that of possible environmental factors affecting employment outcome, especially market factors and presence and quality of rehabilitation.

Prior Reviews of Neuropsychology and Vocational Functioning There are several reviews of the literature on neuropsychological predictors of vocational functioning (Guilmette & Kastner, 1996; Heaton & Pendleton, 1981; Kalechstein, Newton, & van Gorp, 2003; Sbordone & Guilmette, 1999; Sherer et al., 2002). Here we summarize the previous reviews and evaluate new studies since those reviews were published. In one of the first manuscripts to focus on neuropsychological tests and vocational outcome, Heaton and Pendleton (1981) reviewed neuropsychological predictors of everyday functioning, including vocational functioning. In their review they included studies of both normal and impaired populations and described several studies that established the well-­accepted association between vocational functioning and IQ. The general finding from all of the studies of IQ is that unemployed people have lower IQ scores than employed people, and that occupations considered to be of a higher or more challenging level were associated with higher IQ scores (Heaton & Pendleton, 1981). A very important finding in studies in which a correlation is reported between IQ and some index of vocational performance is that IQ is known to correlate approximately .5, accounting for only 25% of the variance in vocational performance. Heaton and Pendleton also described the few studies (at that time) of neuropsychological test performance predicting vocational functioning. They observed that in studies in which the Halstead–­Reitan Battery was used, the average impairment rating was an independent and more powerful predictor of vocational performance or employment status than IQ test scores. They also observed, as reported by Heaton, Chelune, and Lehman (1978), that Minnesota Multiphasic Personality Inventory (MMPI) clinical scales are an additional independent predictor of employment status in a cross-­sectional design (Heaton et al., 1978). When this group

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later attempted to replicate the MMPI findings using a prospective design (Newnan, Heaton, & Lehman, 1978), they found that MMPI results were less predictive of future employment than in their cross-­sectional study. Thus the predictive power of neuropsychological tests was demonstrated more than 25 years ago, but neither IQ nor neuropsychological test scores explain even a majority of the variance in employment status or vocational performance. Guilmette and Kastner (1996) reviewed the literature on neuropsychological tests and prediction of vocational functioning and provided 13 conclusions. Among their conclusions: The greater the degree of impairment, the less employable a person was; neuropsychological tests were better at predicting failure than success; neuropsychological tests should be supplemented with psychosocial/psychological tests to improve predictive validity; and future research would benefit from consistent neuropsychological batteries across studies and validation of brief batteries tailored to specific occupational groups. An important observation of these authors is that because of the limitations of existing studies, the field of neuropsychology is lacking consensus on the predictive power of neuropsychological tests on occupational outcome. They conclude that neuropsychological assessment can predict vocational performance only modestly until further research provides grounds for stronger predictions (Guilmette & Kastner, 1996). As described above, Sherer and colleagues (2002) conducted a rigorous literature review that assessed studies of the predictive power of neuropsychological tests in TBI by using guidelines established by Division 40 of the American Psychological Association for empirical support of neuropsychological practice (Heaton et al., 2002). This review is notable because the quality of the studies was considered in their recommendations. This review of 23 studies revealed that the best prediction of reemployment after TBI occurs when neuropsychological testing is performed soon after posttraumatic amnesia resolves. In addition, they noted that regardless of when neuropsychological testing is performed relative to the injury, the continued presence (i.e., at the time of testing) of neuropsychological impairment is significantly associated with unemployment or decline in quality of employment relative to preinjury status. The authors concluded that the use of neuropsychological testing is strongly supported in the prediction of employment outcome in TBI, especially when neuropsychological testing is performed close in time to the TBI. As noted above, the review emphasized that the time at which neuropsychological assessment is performed is important. Studies in which neuropsychological testing was performed closer in time to the measurement of employment status (i.e., late in recovery process or concurrent with measurement of employment status) did not provide clear evidence that neuropsychological testing is useful in predicting employment status. Although the latter studies contained methodological issues (e.g., small sample size, excessive number of statistical analyses, inadequate sample description) that possibly clouded interpretability of neuropsychological predictiveness, the authors raise the very important issue of the timing of neuropsychological assessment, with the weight of evidence suggesting that in TBI, the earlier neuropsychological testing is conducted the more value it may have in predicting vocational outcome. However, as noted by Sherer (2002, p. 176), “There is no conceptual basis for believing that neuropsychological findings obtained closer in time to assessment of employment outcome should be less predictive of this outcome than neuropsychological findings obtained at an earlier time.” Indeed, the

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authors noted that the studies of late neuropsychological assessment may be of poorer methodology and thus may not have detected a relationship that is actually there. Other authors (e.g., Kalechstein et al., 2003) have used a quantitative, analytical approach to reviewing the literature on the ability of neuropsychological tests to discriminate employed versus unemployed status. Although their meta-­analysis relied on a small number of studies, all seven studies demonstrated at least small effect sizes when employed versus unemployed patients were compared on neuropsychological test scores. The innovative approach of this meta-­analysis is that the authors subdivided the neuropsychological tests from each of the studies into one of eight cognitive domains. Tests in every domain were able to discriminate employed from unemployed persons. The effect sizes were greatest (medium) for the domains of intellectual functioning, executive systems functioning, verbal learning and memory, and nonverbal learning and memory. The smallest effect size was observed for tests of language. The authors reviewed the numerous limitations of this kind of analysis, including the inability to account for job complexity, demographic factors, and so on. Nevertheless, the important contribution of this study is the finding that abilities in some neuropsychological areas may be better at discriminating employment status than others.

Recent Studies of Neuropsychology and Vocational Functioning Ever since Heaton and colleagues (1978) reported discriminant function analyses that neuropsychological test scores, in combination with MMPI scales, could classify employment status in greater than 80% of their mixed clinical sample, there has been an abundance of research on the association between neuropsychological test scores and employment, as can be seen from the reviews described above. In this section we review several studies that were not included in the published reviews to provide an updated summary of the state of the literature. One research group (Machamer et al., 2005) went beyond the simple prediction of return to work by observing the stability of employment after TBI in a sample of 165 consecutively admitted patients. As noted above, they measured employment stability as number of months worked full time, number of full-time jobs held, and maintenance of employment once returned to full-time work. Their sample was largely a mild-to-­moderate TBI group, with more than half of their subjects having a Glasgow Coma Scale (GCS) of 13–15 (mild) and being able to follow commands within 24 hours. Neuropsychological testing was conducted 1 month postinjury, and the post injury follow-up period was between 3 and 5 years for the assessment of employment stability. When they categorized patients by percent of time worked during the follow-up period (0%, 1–50%, 51–89%, and ≥ 90%), they found that less time worked was associated with lower GCS scores, longer time postinjury before commands were followed, worse neuropsychological performance, and preinjury job instability. They used stepwise multiple regression to predict percent of time worked half-time or more and maintenance of employment. The models included the following predictors: age, education, gender, preexisting conditions, preinjury yearly earnings, preinjury work stability, TBI severity evaluated by time to follow commands, other system injury severity, and neuropsychological outcome at 1-month postinjury.

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The model that explained the most variance (r 2 = .428) in predicting percent of time worked during the follow-up period included the digit symbol test, preinjury earnings, and preinjury arrest record. Maintenance of employment was best predicted (logistical regression) by Performance IQ score, arrest record, and preinjury earnings. This study is important because it goes beyond the simple outcome measure of employed versus unemployed and provides evidence that postinjury work stability is uniquely associated with neuropsychological test performance even when many preinjury factors and injury severity are taken into account. The use of stepwise regression has its problems, including the fact that the final model does not say how well neuropsychological test scores predict work outcomes when injury characteristics are controlled. Doctor and colleagues (2005) presented a novel analysis of TBI employment outcome by comparing 1-year relative risk ratios for a TBI sample and a control sample on failure to return to work. This approach is significant because the rate of return to work is compared to that of the general population to answer the question of whether unemployment is greater than that for the population in general. As might be expected, having a TBI presented more than a fourfold increase in unemployment risk compared to the general population, and lower test scores 1-month postinjury were significantly associated with greater risk for unemployment. Unfortunately, multivariate analyses were not conducted to determine if neuropsychological impairment was uniquely predictive of unemployment risk, although this question has been addressed by other studies. Careful selection of participants contributes to the ability of a study to draw conclusions about predictors of employment outcomes. Cattelani, Tanzi, Lombardi, and Mazzucchi (2002) studied a group of patients with TBI involving the severest injuries, as indicated by a GCS score of 3–8 on admission (severe), coma of at least 3 days, and posttraumatic amnesia (PTA) of at least 7 days. The group was also well selected to rule out premorbid confounding factors to future employment: All were employed or in school and none had a history of substance abuse or dependence, mental retardation, psychiatric disorder, or were undergoing treatment for any “situational or psychosocial problems.” They divided their 35 eligible participants into demographically matched groups of 19 reemployed (resumed preinjury employment or academic status) and 16 non-­reemployed groups. The authors observed that TBI characteristics, including combined PTA and coma duration, distinguished the two groups (28 days for reemployed vs. 108 days for non-­reemployed). They observed that worse early activities of daily living (ADL) problems (Barthel Index) predicted worse resumption of preemployment vocational status. Although they also found that lower Wechsler Adult intelligence Scale—­Revised (WAIS-R) scores and lower examiner ratings of neuropsychological abilities predicted worse outcome, they did not perform any analyses to determine if cognitive performance predicted outcome independently of TBI severity.

Specific Neuropsychological Abilities That Predict Vocational Outcome One problem across the spectrum of studies of neuropsychological and vocational outcome is the variability in the test batteries administered (Guilmette & Kastner,

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1996). While norming different batteries for different populations or purposes (Ryan, Morrow, Bromet, & Parkinson, 1987) is one solution, another solution is to routinely include a summary score across the entire battery (like a mean t-score, deficit score, or domain summary scores) that can be used to predict outcome. Research with HIV-infected adults (Carey et al., 2004) provides evidence that sufficient sensitivity and specificity can be achieved by neuropsychological batteries that contain different tests but that, when average impairment across tests is used, the different batteries can yield similar classification rates of impairment. This research suggests that the use of summary scores may generalize across batteries and might be an approach to overcome some methodological variability across studies. Average impairment in this study was measured using the deficit score approach, as outlined in a widely used normative manual (Heaton, Miller, Taylor, & Grant, 2007), in which demographically corrected T-scores are assigned a degree of deficit on a scale of 0 (no deficit) to 5 (severe deficit) in 5-point decrements in the T-score. A “global deficit score” is calculated as the average deficit score across all measures and serves as an index of overall impairment. In another example, Newnan and colleagues (1978) found that a cutoff score of 1.61 on the Russell Average Impairment Rating classified 78% of their subjects as employed or unemployed, with a positive predictive value of 81% and a negative predictive value of 70%. Indeed, the research does not support the predictive validity of individual tests for outcomes and diagnoses, but there is substantial evidence that IQ scores and summary scores can predict outcomes. On the other hand, different batteries (including psychosocial measures) may need to be developed for different purposes. There are two considerations here: first, that a neurological problem may have unique neuropsychological ability deficits, and that these disease-­specific deficits may explain vocational problems. For example, in TBI, memory and attention were significant predictors of employment outcome (Brooks, McKinlay, Symington, Beattie, & Campsie, 1987), whereas in mental retardation motor and vocabulary predicted better functional independence (Blackwell, Dial, Chan, & McCollum, 1985). In multiple sclerosis unemployment is best predicted by verbal memory and numerical reasoning (Paced Auditory Serial Addition Test; Benedict et al., 2006), whereas in HIV, verbal learning (California Verbal Learning Test, total trials 1–5) was the strongest predictor of return to work (van Gorp et al., 2007). Second, psychosocial and behavioral factors such as those measured by the MMPI-2, Beck Depression Inventory (BDI), or the various frontal system questionnaires (Malloy & Grace, 2005) can add to or even replace predictive validity of neuropsychological performance (Heaton et al., 1978). Again, disease-­specific factors may dictate the tools that provide the best predictive validity. Further research is required before the impact of emotional versus cognitive factors—­particularly in clinical situations where psychological distress is more salient than cognitive impairment—is fully understood in each clinical condition (Heaton et al., 2004). One of the most thoroughly studied psychiatric populations with regard to neuropsychological predictors of return to work is schizophrenia. As a significant public health problem, it is natural that schizophrenia, whose symptoms can be treated pharmacologically, is a target for rehabilitation. Some studies (e.g., McGurk & Mueser, 2003) have reported that intact executive functioning and verbal learning predict more wages earned and more hours worked over a 2-year follow-up period. In addi-

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tion, this group reported that worse cognitive abilities predicted greater utilization of supported employment services, suggesting that cognitive impairment results in more rehabilitation resource consumption. This same group followed their sample through 4 years of supported employment (McGurk & Mueser, 2006). Cognitive and symptom measurements taken 2 years after beginning a work rehabilitation program were correlated with total hours of competitive work 3–4 years after beginning the program and total wages earned during that period. The authors observed that both of these markers of employment (total hours and wages) were correlated with symptoms (autistic preoccupation from the Positive and Negative Symptom Scale [PANSS]) and cognitive performance. The specific cognitive abilities that predicted more hours and higher wages included executive functioning (Wisconsin Card Sorting Test [WCST] percent perseveration, Trail Making Test Part B), working memory (letter–­number sequencing, digit span backward, digit span forward), and speed of information processing (Trail Making Test A, digit symbol substitution). Unfortunately, the authors did not perform a multivariate analysis to determine whether neuropsychological test performance independently predicted work performance when psychiatric symptoms were taken into account. And, similar to their 2-year follow-up study, their 4-year follow-up data also showed that worse performance on neuropsychological tests predicted greater utilization of supported employment services, including contact hours with counselors. Studies of cognitive predictors of employment that also use a work performance measure are rare. One study of employment outcome involved a sample of 112 patients with schizophrenia who enrolled in work rehabilitation programs (Evans et al., 2004). This study utilized an inventory that involves ratings by a research assistant directly observing the patient’s work performance and by interview with the patient’s immediate supervisor (Work Behavior Inventory; Bryson, Bell, & Lysaker, 1997). This study also measured employment status using the Work Placement Scale (Meyer, Bond, Tunis, & McCoy, 2002) consisting of a 5-point gradient ranging from integrated employment to unemployed, with higher scores representing more independent employment activity. Although the study is complicated by a heterogeneous sample (patients from different vocational rehabilitation programs) and incomplete data (work performance measures were available only for a subsample from one vocational program), the pattern of neuropsychological predictors of work placement and work performance was consistent: baseline verbal learning and memory were related to 4-month vocational outcomes regardless of the outcome measure. Interestingly, executive functioning (WCST and Trail Making Test Part B) was not associated with outcome. Many people posit that executive functioning deficits in schizophrenia are central to the disability associated with the disease (Iachini, Sergi, Ruggiero, & Gnisci, 2005; Nuechterlein et al., 2004; Sergi, Kern, Mintz, & Green, 2005; Velligan, Bow-­Thomas, Mahurin, Miller, & Halgunseth, 2000). This research raises the possibilities that executive functioning is not a core deficit or that the executive functioning ability is a complex construct that is better explained by core components such as learning. It has also been reported that better immediate memory predicted full- or part-time employment status in a sample of patients with bipolar disorder, although executive functioning was not thoroughly assessed in this sample (Dickerson et al., 2004).

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Predicting Vocational Outcomes Independent of Disease Variables From the studies reviewed above, it is clear that neuropsychological tests do predict employment status following brain disease or injury. In general, most studies have not attempted to demonstrate whether neuropsychological abilities uniquely predict vocational functioning above and beyond easily obtained indices of disease status. The central question that should be asked is whether neuropsychological tests are necessary to serve as predictors once disease variables are known. Verbal learning (California Verbal Learning Test [CVLT] total trials 1–5) independently predicted return to work 2 years after initial assessment in a sample of 118 prospectively studied HIV-infected persons who were seeking to return to work, even when disease severity (AIDS status, viral burden, immunosuppression) and education level were taken into account (van Gorp et al., 2007). In a TBI sample Novack and colleagues analyzed 1-year outcome data using path analysis, which is a form of structural equation modeling that, because of its multivariate nature, is well suited to answering the question of whether neuropsychological performance predicts outcome when other factors, such as premorbid status and disease characteristics, are taken into account (Novack, Bush, Meythaler, & Canupp, 2001). In this model premorbid status, injury severity, and 6-month cognitive, emotional, and functional status were used to predict 12-month outcome. The 12-month outcome factor included the Community Integration Questionnaire, Disability Rating Scale, and “productivity level” (employment or high school/college enrollment). One advantage of path analysis is that measured variables, such as individual neuropsychological test scores or specific questionnaire scores, can be grouped together into conceptual categories called “endogenous variables.” Then direct and indirect relationships among the endogenous variables can be estimated using path analysis to determine which constructs are or are not significant direct predictors of the outcome. These researchers found that the only significant direct predictors of outcome were cognitive status (consisting of nine cognitive and five observation-based neurobehavioral rating scores from the Neurobehavioral Rating Scale) and premorbid factors (consisting of age, education, employment status, and alcohol, drug, and social histories). Importantly, injury severity and premorbid factors were significantly predictive of cognitive status and thus only indirectly associated with TBI outcome (including employment). This study is an excellent example of the utility of neuropsychological assessment to predict future vocational functioning even when disease characteristics are considered. These findings were replicated with a separate sample by the same group (Bush et al., 2003).

Performance-Based Assessment of Vocational Functioning As described at the beginning of this chapter, there are many performance-based assessments for career planning, applicant screening, and job placement purposes used primarily by persons involved in vocational and career planning outside a clinical context. There are few performance-based assessments of vocational functioning specifically designed for rehabilitation of clinical populations; the Behavioral Assessment of Vocational Skills (Butler, Anderson, Furst, & Namerow, 1989) is one such test, and it consists of a standardized measurement of a person’s ability to assemble

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a wheelbarrow using printed instructions. Trained examiners rated patients on their ability to follow directions, maintain their attention, tolerate frustration, and on several other variables in the face of preplanned interruptions and criticisms by the examiner. The test was designed to mimic actual demands faced in a typical manual labor work environment, including a predefined goal and the presence of common distracting events. In their sample of 20 participants with brain injury, this test predicted ratings of employment performance during a 3-month trial work placement, independent of neuropsychological test scores. We await additional validation data on the instrument, as few other studies that have used this measure to date. Commercially developed vocational assessment instruments have been studied in neuropsychologically impaired persons with HIV infection (Heaton et al., 1994). The instrument (the COMPASS; Valpar International Corporation, Tucson, AZ) purports to assesses work skills, although in reality it is weighted toward general cognitive and motor skills (e.g., reasoning, arithmetic, language comprehension, immediate memory) that are then scored along dimensions deemed important work-­related skills and abilities by the 1991 DOT (described above). The addition of manual tasks that require fine motor control (wiring task) as well as sequencing and upper limb dexterity (machine tending and alignment and driving tasks) broadens the scope to behaviors not assessed with traditional neuropsychological tests. Because the study was cross-­sectional, the findings are limited to a snapshot in time that supports the validity of neuropsychological impairment as a significant predictor of lower scores on the COMPASS. In this study there was no direct assessment of whether COMPASS scores were worse in the unemployed participants. In a later manuscript that included the COMPASS in a larger battery of everyday functioning tests, Heaton and colleagues (2004) observed that failing scores on the COMPASS (relative to a neuropsychologically normal group with HIV infection) were associated with lower scores in the neuropsychological domains of Abstraction/ Mental Flexibility (Category Test, Trail Making Test Part B) and Working Memory (WAIS-III Digit Span and Arithmetic, and Paced Auditory Serial Addition Test). In addition to the ability of the COMPASS to serve as a performance-based assessment of vocational skills, its integration with the DOT allows comparison of a person’s current level of vocational abilities measured by the COMPASS with an estimate of abilities the person had at his or her highest occupational achievement. An estimate of decline can be obtained, in addition to an estimate of the number of jobs a person might be able to perform both at his or her highest functioning and at the measured functioning. As the first study demonstrated, it is possible to quantify the loss of job opportunities as a result of measured decline in functional abilities (Heaton et al., 1994). These studies suggest that performance-based assessment can be associated with cognitive abilities. However, further research is required to determine whether the COMPASS predicts unemployment (which seems likely), and if the COMPASS or any other performance-based vocational measure can predict work performance in an employed clinical sample. There are no studies that address which type of assessment—neuropsychological testing or performance-based vocational testing—has better predictive validity of employment status or long-term employment outcomes. One group compared neuropsychological test performance to ratings and recommendations of a trained vocational expert when the patient performed simulated work tasks (Leblanc, Hayden, & Paulman, 2000). These authors determined that

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general cognitive abilities (WAIS-R, combinations of various neuropsychological tests) best predicted expert employment performance ratings. Consistent with the notion that individual tests are not useful predictors, no single neuropsychological test or ability correlated significantly with the vocational evaluator’s recommendation that a person could or could not return to competitive employment. However, specific neuropsychological tests were associated with specific ratings of performance on simulated work activities. For example, Wechsler Memory Scale Logical Memory II (delayed recall) was associated with examiner ratings of patient memory during work tasks, and a number of tests in their executive functioning factor (Rey Complex Figure, Booklet Category Test, WAIS-R Object Assembly and Block Design) were associated with examiner ratings of visual and verbal problem solving. While the data appear to provide some evidence for domain-­specific ecological validity of the neuropsychological tests, the vocational expert was not blind to the neuropsychological test findings, and indeed based the choice of vocational tasks in part on the neuropsychological profile. This aspect of the methodology suggests that the examiner’s ratings (the outcome variables of the study) could have been biased by the neuropsychological test scores (the predictor variables). The study did not assess whether neuropsychological or performance-based ratings predicted actual work outcomes in this TBI sample. Future studies should address the relative value of vocational rehabilitation approaches to measuring work outcomes. In summary, there is little research on performance-based vocational assessment in clinical populations. Because “vocational functioning” encompasses such a broad domain that includes physical as well as cognitive abilities, and because it is impossible to design a single test or series of tests to measure the myriad of complex work skills required in today’s occupations, it is not surprising that research in this area is scant. However, some studies in the human factors arena (e.g., Gonzalez, 2005; see also Rogers et al., Chapter 2, this volume) hold promise for development of computerized assessment tools that can simulate specific job demands and obtain objective data with normative reference. More importantly, such approaches can help define new conceptual cognitive domains (e.g., dynamic decision making; Edwards, 1962) that might have better predictive validity for vocational performance than traditional neuropsychological domains.

Focus of Future Research to Improve Predictive Validity As we have noted (Sherer et al., 2002), group studies consistently find that neuropsychological test results are statistically associated with employment status and vocational outcomes. However, the predictive validity of specific neuropsychological tests is often lacking in these studies. It would be very useful, for example, for future research to utilize statistical approaches such as discriminant function, odds ratios, positive and negative predictive values, or other classification approaches to determine how accurate neuropsychological performance is when disease-­specific variables (e.g., PTA and loss of consciousness in TBI, AIDS status in HIV, or lesion location and volume in stroke), demographic and premorbid variables, and neuropsychological performance (e.g., mean T-score or deficit score) in statistical models predicting the outcome of interest, such as return to premorbid employment status or likelihood

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of any postdisease employment. A major criticism of most of the cited studies is that they are conducted on groups of participants, with the primary conclusion that neuropsychological tests are predictive of vocational functioning on average, with little data that can be applied to the individual about his or her current or future prospects for employment. The exception to this rule is found in the cases of severely impaired individuals, where gainful employment is highly unlikely. The most useful studies will address how patients who are not completely disabled and who have some preserved abilities can be helped in vocational rehabilitation. A second focus of future research should examine remediation of cognitive deficits and assessment of the effectiveness of the remediation. An example of such research is offered by Jensen and colleagues, who treated reading and writing impairments in a sample of individuals with learning disabilities and demonstrated positive outcomes on employment measures (Jensen, Lindgren, Andersson, Ingvar, & Levander, 2000). The often stated claim that neuropsychological profiles can provide a “map” of cognitive strengths and weaknesses that can then guide rehabilitation is a claim that is largely unsubstantiated, even if ultimately proven to be true. Although several studies have found associations between baseline cognitive abilities and vocational outcomes some time later (Evans et al., 2004; McGurk & Mueser, 2006; van Gorp et al., 2007), no studies to our knowledge have assessed whether individualized treatment plans based on neuropsychological profiles improve employment outcomes. A third focus of future research is the development of task-­independent standardized rating scales that would formally rate work behaviors that are directly observed. The first step in this direction has been developed by LeBlanc and colleagues (2000) and is called a “situational vocational evaluation” (SEval). In its current form, as described above, a certified vocational evaluator has the subject perform simulated work activities, and then rates his or her performance on 16 indices in one of three general categories (visual processing, memory, executive functioning). The main problems requiring further research with this approach include developing standardized rating criteria that would result in sufficient interrater reliability and ensuring that all relative domains were assessed. If such a generalized rating system could be developed, then the clinician would have a standard instrument (much like the Functional Independence Measure [FIM] that is widely used in physical and occupational therapy outcome studies) that could be applied regardless of the specific vocation. A fourth focus of future research should exploit technological advances such as virtual environments or virtual reality. The human factors literature, including such studies as Gonzales (2005) and Gonzalez and colleagues (2005) and that exemplified in a special issue of the International Journal of Human–­Computer Studies on “Interaction with Virtual Environments” (Volume 64, Issue 3), provides some promising directions, but as noted by Wilson (2006, p.  157), “human factors empirical work has been extraordinarily difficult to plan and carry out, given the very large number of variables involved.” We agree with many of the conclusions made by Sbordone, including these: (1) Individual predictions of vocational abilities need to be weighted by the facts that no neuropsychological test score can accurately predict vocational performance, and that neuropsychological testing as a predictor of vocational ability should be interpreted with caution since the procedure is not an actual measure of vocational performance and since the testing situation is rarely similar to the actual employment

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environment; and (2) many factors other than neuropsychological test scores need to be considered when predicting vocational abilities, such as preinjury work performance and job stability, past or current substance abuse, psychological disorders and stressors, and any medical, neurological, or developmental disorders (Sbordone, 2001). The more these issues can be addressed with an empirical approach, the less guesswork will be required and the less uncertainty will result from current treatment of individuals with acquired brain disorders.

Acknowledgments We would like to thank Patrick Cordova and Christine Karver for their help in preparing this chapter.

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Kendall, E., & Terry, D. (1996). Psychosocial adjustment following closed head injury: A model for understanding individual differences and predicting outcome. Neuropsychol Rehabil, 6, 101–132. Klonoff, P. S., Lamb, D. G., & Henderson, S. W. (2001). Outcomes from milieu-based neurorehabilitation at up to 11 years post-­discharge. Brain Inj, 15, 413–428. Leblanc, J. M., Hayden, M. E., & Paulman, R. G. (2000). A comparison of neuropsychological and situational assessment for predicting employability after closed head injury. J Head Trauma Rehabil, 15, 1022–1040. Lees-Haley, P. R. (1990). Vocational neuropsychological requirements of U.S. occupations. Percept Mot Skills, 70, 1383–1386. Machamer, J., Temkin, N., Fraser, R., Doctor, J. N., & Dikmen, S. (2005). Stability of employment after traumatic brain injury. J Int Neuropsychol Soc, 11, 807–816. Malloy, P., & Grace, J. (2005). A review of rating scales for measuring behavior change due to frontal systems damage. Cogn Behav Neurol, 18, 18–27. McGurk, S. R., & Mueser, K. T. (2003). Cognitive functioning and employment in severe mental illness. J Nerv Ment Dis, 191, 789–798. McGurk, S. R., & Mueser, K. T. (2004). Cognitive functioning, symptoms, and work in supported employment: A review and heuristic model. Schizophr Res, 70, 147–173. McGurk, S. R., & Mueser, K. T. (2006). Cognitive and clinical predictors of work outcomes in clients with schizophrenia receiving supported employment services: 4-year follow-up. Adm Policy Ment Health, 33, 598–606. Meyer, P. S., Bond, G. R., Tunis, S. L., & McCoy, M. L. (2002). Comparison between the effects of atypical and traditional antipsychotics on work status for clients in a psychiatric rehabilitation program. J Clin Psychiat, 63, 108–116. Newnan, O. S., Heaton, R. K., & Lehman, R. A. (1978). Neuropsychological and MMPI correlates of patients’ future employment characteristics. Percept Mot Skills, 46, 635–642. Novack, T. A., Bush, B. A., Meythaler, J. M., & Canupp, K. (2001). Outcome after traumatic brain injury: Pathway analysis of contributions from premorbid, injury severity, and recovery variables. Arch Phys Med Rehabil, 82, 300–305. Nuechterlein, K. H., Barch, D. M., Gold, J. M., Goldberg, T. E., Green, M. F., & Heaton, R. K. (2004). Identification of separable cognitive factors in schizophrenia. Schizophr Res, 72, 29–39. Nybo, T., Sainio, M., & Muller, K. (2004). Stability of vocational outcome in adulthood after moderate to severe preschool brain injury. J Int Neuropsychol Soc, 10, 719–723. Ownsworth, T., & McKenna, K. (2004). Investigation of factors related to employment outcome following traumatic brain injury: A critical review and conceptual model. Disabil Rehabil, 26, 765–783. Ruffolo, C. F., Friedland, J. F., Dawson, D. R., Colantonio, A., & Lindsay, P. H. (1999). Mild traumatic brain injury from motor vehicle accidents: Factors associated with return to work. Arch Phys Med Rehabil, 80, 392–398. Ryan, C. M., Morrow, L. A., Bromet, E. J., & Parkinson, D. K. (1987). Assessment of neuropsychological dysfunction in the workplace: Normative data from the Pittsburgh Occupational Exposures Test Battery. J Clin Exp Neuropsychol, 9, 665–679. Sbordone, R. J. (2001). Limitations of neuropsychological testing to predict the cognitive and behavioral functioning of persons with brain injury in real-world settings. NeuroRehabilitation, 16, 199–201. Sbordone, R. J., & Guilmette, T. J. (1999). Ecological validity: Prediction of everyday and vocational functioning from neuropsychological test data. In J. J. Sweet (Ed.), Forensic neuropsychology: Fundamentals and practice (pp. 227–254). Lisse, Netherlands: Swets & Zeitlinger.

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Sergi, M. J., Kern, R. S., Mintz, J., & Green, M. F. (2005). Learning potential and the prediction of work skill acquisition in schizophrenia. Schizophr Bull, 31, 67–72. Sherer, M., Novack, T. A., Sander, A. M., Struchen, M. A., Alderson, A., & Thompson, R. N. (2002). Neuropsychological assessment and employment outcome after traumatic brain injury: A review. Clin Neuropsychol, 16, 157–178. Skeel, R. L., Bounds, T., Johnstone, B., Lloyd, J., & Harms, N. (2003). Age differences in a sample of state vocational rehabilitation clients with traumatic brain injury. Rehabil Psychol, 48, 145–150. Tsang, H. W. H. (2003). Augmenting vocational outcomes of supported employment with social skills training. J Rehabil, 69, 25–30. Twamley, E. W., Narvaez, J. M., Sadek, J. R., Jeste, D. V., Grant, I., & Heaton, R. K. (2006). Work-­related abilities in schizophrenia and HIV infection. J Nerv Ment Dis, 194, 268– 274. U.S. Department of Labor. (1991). Dictionary of occupational titles (4th ed.). Washington, DC: Government Printing Office. U.S. Department of Labor. (2006). O*Net Online: Occupational information network. Available at online.onetcenter.org. van Gorp, W. G., Rabkin, J. G., Ferrando, S. J., Mintz, J., Ryan, E., Borkowski, T., et al. (2007). Neuropsychiatric predictors of return to work in HIV/AIDS. J Int Neuropsych Soc, 13, 80–89. Velligan, D. I., Bow-­T homas, C. C., Mahurin, R. K., Miller, A. L., & Halgunseth, L. C. (2000). Do specific neurocognitive deficits predict specific domains of community function in schizophrenia? J Nerv Ment Dis, 188, 518–524. Wilson, B. A. (1992). Recovery and compensatory strategies in head injured memory impaired people several years after insult. J Neurol, Neurosur Ps, 55, 177–180. Wilson, B. A. (2000). Compensating for cognitive deficits following brain injury. Neuropsychol Rev, 10, 233–243. Wilson, B. A., & Watson, P. C. (1996). A practical framework for understanding compensatory behaviour in people with organic memory impairment. Memory, 4, 465–486. Wilson, J. R. (2006). Interaction with virtual environments. Int J Hum-­C omput St, 64, 157. Wood, R. L., & Rutterford, N. A. (2006). Demographic and cognitive predictors of longterm psychosocial outcome following traumatic brain injury. J Int Neuropsychol Soc, 12, 350–358. World Health Organization. (2001). International classification of functioning, disability, and health. Geneva: World Health Organization.

Chapter 6

Medication Management Terry R. Barclay, Matthew J. Wright, and Charles H. Hinkin

M

edication adherence is broadly defined as the accurate use of medication and refers to proper administration of medicine in the correct dosage, at the appropriate time, and in accordance with any special instructions (Gould, McDonald-­Miszczak, & Gregory, 1999). Proper medication adherence can prevent the deleterious effects of many chronic illnesses and is generally associated with improved health over a longer period of time. For example, it has been demonstrated that consistent antihypertensive therapy is associated with a 35–40% lower incidence of stroke, a 20–25% reduction in myocardial infarction, and a more than 50% reduction in heart failure (Neal, MacMahon, & Chapman, 2000). Despite the many benefits associated with adequate medication adherence, however, existing literature suggests that compliance with medication regimens is at best moderate and tends to decline over time in almost all chronic diseases (Dunbar-Jacob, 2002). In fact, although medication adherence is critical to the health maintenance of many individuals, rates of compliance are lower than 50% in most studies (for review, see Dunbar-Jacob et al., 2000; Haynes, McDonald, Garg, & Montague, 2003). Inadequate compliance with medication regimens has been shown to be associated with a host of untoward consequences, including declines in overall health and increased risk of hospitalization (Col, Fanale, & Kronholm, 1990; Hallas et al., 1992), increased morbidity and mortality (Callahan & Wolinsky, 1995; Ganguli, Dodge, & Mulsant, 2002), and higher health care costs (Gryfe & Gryfe, 1984). Indeed, the estimated financial burden of nonadherence is staggering. The U.S. Department of Health and Human Services (1990) has estimated that approximately 10% of hospital admissions are directly attributable to nonadherence with prescription drug regimens, and 23% of nursing home admissions are seen as secondary to poor compliance with medical regimens. It has been estimated that medication nonadherence alone may have a direct economic cost of at least $100 billion annually. Clearly, determining the causes of poor medication adherence and using that knowledge to structure effective interventions are critically needed. 136

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The existing literature has demonstrated that nonadherence is a complex, multidimensional problem. Previous studies have found poor medication adherence to be associated with a host of factors, including neurocognitive status, alcohol and drug use, psychiatric disturbance, insight and judgment, the class of prescribed medication, regimen complexity, drug efficacy, the route of administration, occurrence of negative side effects, type and chronicity of disease/illness, human factors (e.g., packaging and labeling of medication bottles, grade level at which health-­related materials are written), physician interaction and communication style, financial resources, level of daily activity, degree of social isolation, family support, beliefs and attitudes regarding one’s health, and level of health literacy. Whereas the majority of early studies focused on single constructs as possible determinants of nonadherence, more recent investigations have begun to incorporate broader, and more elaborate, models of medication-­taking behavior. This chapter presents a broad overview of current knowledge regarding the complex nature of adherence to medication regimens and those factors most clearly associated with individuals’ medication-­taking behavior. In doing so, we begin with a critical review of medication adherence methodologies and measurement techniques, including clinician ratings, self-­report measures, pill counts, pharmacy records, electronic monitoring, physiological measurements such as blood tests, and laboratorybased analogue measures. An examination of medication adherence behaviors in select neurocognitive disorders then follows, with special attention paid to research conducted in the areas of normal aging, dementia, HIV/AIDS, and psychiatric illness. These disorders were chosen because they represent well the varied literature in this area and because they illustrate many of the common problems associated with medication nonadherence in those with impaired cognitive abilities. Also included is a brief review of the major psychosocial models that have been used to explain adherence behavior, including theories related to autonomy and self-­efficacy, treatment expectancies, the health beliefs model, the theory of reasoned action, and social action theory. The chapter concludes with an evaluation of various medication management interventions and a discussion of future directions in medication adherence research.

Adherence Methodologies and Measurement Techniques A number of techniques that has been used to measure medication adherence, all of which are characterized by unique strengths and weaknesses. These can be divided into techniques that provide more objective measures, such as plasma drug levels and electronic measuring devices, and those that provide more subjective information, such as patient self-­report or clinician ratings.

Biological Markers Blood levels provide precise quantification of adherence for medications that have a long half-life. For example, blood tests are an excellent means for ascertaining lithium levels and, by extension, whether patients with bipolar disorder are indeed taking their lithium carbonate as prescribed. In contrast, blood tests are not as useful

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for evaluating adherence rates for medications that are rapidly metabolized. In such cases blood levels can detect whether patients have recently taken their medication but cannot assist in determining whether patients typically take their medication, as prescribed.

Pill Counts Pill counts are another technique that have been used to measure adherence rates. The technique is relatively straightforward. If one knows how many pills a patient initially possessed and how many pills he or she should have ingested in the intervening time period, it is easy to calculate the number of pills that should remain at the end of the study period. Excess doses are therefore considered to reflect doses not taken as prescribed. For example, consider a patient on a 3 pills/day regimen who begins with 100 pills and returns to clinic 30 days later. If 10 pills remain, that would be interpreted as perfect adherence (100 – (30 × 3) = 10). While easy for the researcher/clinician to calculate, a decided drawback is that this is also easy for patients to calculate as well. Accordingly, prior to their return to clinic, patients may remove extra doses from their pill bottle and thus appear more adherent than is actually the case. An innovative approach to overcome this limitation has been introduced by David Bangsberg and colleagues at the University of California at San Francisco (UCSF) (Bangsberg, Hecht, Charlebois, Chesney, & Moss, 2001). They conduct “unannounced pill counts,” appearing at participants’ residences without warning to conduct adherence assessments. They have found this approach to correlate well with biological outcomes (e.g., HIV viral load, or the amount of virus circulating in the blood). Although this methodology works well in a dense urban community such as San Francisco or New York, it would be excessively cumbersome for use in sparsely populated rural settings or in a sprawling metropolis without public transportation, such as Los Angeles.

Self-­Report Self-­report is another widely used methodology. Strengths of self-­report include its negligible cost and ease of data collection. Conversely, a weakness of self-­report measures is that, for a multitude of reasons, many patients may overstate their actual adherence rates. For example, studies of HIV-infected adults have revealed that patient self-­report, relative to electronic monitoring techniques, tends to be accurate among patients who candidly admit to poor adherence but may overestimate actual adherence rates by approximately 10–20% among a large subset of patients who claims perfect or near-­perfect adherence (Arnsten et al., 2001; Levine et al., 2005).

Electronic Measuring Devices The fallibility of self-­report may be particularly salient when dealing with individuals who have memory impairment. Individuals with a dementing disorder may encounter considerable difficulty remembering whether or not they took their medication as prescribed. This inability is particularly pronounced when self-­reported adherence rates are queried for more distal time periods. For this reason, the utilization of

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electronic monitoring devices (e.g., Medication Event Monitoring System [MEMS], Aprex Corp, Union City, CA) may better estimate actual adherence. MEMS embeds a computer chip in the cap of a pill bottle that automatically records the date, time, and duration of pill-­bottle opening. Although electronic monitoring devices are not a perfect measure of medication adherence, a number of studies has shown that they may be more accurate than pill counts or self-­report, both of which appear to significantly overestimate adherence rates. Drawbacks of this method include the bulky nature of the MEMS cap bottle, which precludes inconspicuous transportation of one’s medications. This can lead to behavior called “pocket-­dosing” in which patients remove an extra dose from their pill bottle to consume at a later point in time rather than carry their pill bottle with them. Also, in the past, the use of MEMS devices has precluded use of daily/weekly pill organizers, although technological advances are now emerging that will overcome this limitation in the future.

Pharmacy Refill Records Pharmacy refill records have also proven to be a cost-­effective proxy for measuring medication adherence. This technique rests on the assumption that if patients are refilling their medication prescriptions in a timely fashion, then they are more likely to be taking their medication as prescribed, as compared to individuals who are tardy in refilling their prescriptions. This approach works best in settings where pharmacy records are centralized and can be easily attained (e.g., in Veterans Administration Medical Centers).

Laboratory-Based Analogue Measures In addition to attempts to assess real-world medication adherence, several investigators have utilized laboratory-based measures thought to be reflective of individuals’ ability to adhere to medical recommendations. Interestingly, two groups (Albert et al., 1999; Gurland, Cross, Chen, & Wilder, 1994) independently developed such analogue measures of medication management skills by the same name: the Medication Management Test (MMT). Gurland and colleagues’ (1994) version (MMT-Gurland) was primarily developed to assess the ability of older adults to self-­administer medications, whereas Albert and colleagues’ (1999) edition (MMT-Albert) was created to assess medication management skills among HIV-infected individuals. Both tests entail sorting, organizing, and making inferences about fictitious medications (e.g., when a prescription would need to be refilled). The MMT-Albert is more in-depth and requires 15–25 minutes to administer; the MMT-Gurland takes approximately 5 minutes to administer. The MMT-Gurland has been shown to be associated with cognitive decline in older adults (Fulmer & Gurland, 1997; Gurland et al., 1994). The MMT-Albert has been associated with cognitive deficits in HIV-infected individuals, specifically difficulties in memory and executive and motor functioning (Albert et al., 1999, 2003). The MMT-Albert has been further revised by Patterson and colleagues (2002; Medication Management Ability Assessment [MMAA]) and Heaton and colleagues (2004; MMT—Revised [MMT-R]). Heaton and colleagues’ adaptation included reordering test items by ascending order of difficulty, rewording some test items and the mock

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medication insert, as well as reducing the number of fictitious medications (from five to three) and inference items (from fifteen to seven). The MMT-R requires approximately 10 minutes to administer and has been shown to correlate with neuropsychological deficits in executive function and memory in HIV-infected individuals (Heaton et al., 2004). To better characterize the possible medication management problems faced by individuals suffering from schizophrenia, the MMAA was modified from the MMT-Albert to better mimic interactions between patients and prescribing physicians (Patterson et al., 2002). It also requires examinees to demonstrate how they would self-­administer medications after a 1-hour delay. Performance on the MMAA has been associated with memory and executive abilities of participants with schizophrenia (Jeste et al., 2003; Patterson et al., 2002). Interestingly, the MMAA was recently studied in relationship to a virtual reality (VR) task designed to simulate the medication-­taking environment of participants with schizophrenia (Baker, Kurtz, & Astur, 2006). Like the MMAA, the experimental VR task correlated with memory and executive functioning, but it also showed significant relationship with sustained attention. Finally, direct observation has also been used (e.g., in tuberculosis programs), but it is prohibitively expensive in all but select cases.

Review of Medication Adherence in Select Neurocognitive Disorders Normal Aging Older adults experience more chronic illness and consume more medications than any other age group (see Ball et al., Chapter 10, this volume; Huang et al., 2002; Matteson & McConnell, 1988; Williams & Kim, 2005). In fact, 87–92% of patients over the age of 65 regularly take some form of medication (Gryfe & Gryfe, 1984). The number of drugs taken increases when patients become institutionalized or enter residential care. Between 67 and 80% of noninstitutionalized ambulatory older adults may receive drugs, but in nursing homes, the consumption rate can be as high as 97% (Ray, Federspiel, & Schaffner, 1980). Unfortunately, considerably higher rates of noncompliance have been reported among older patients. Estimates have ranged from 40 to as high as 75% (Ostrum, Hammarlund, Christensen, Plein, & Kethley, 1985), and adherence seems to be particularly problematic for commonly prescribed agents such as antihypertensive medications, lipid-­lowering drugs, and antiarthritic medications. As many as 10% of older adults take drugs prescribed for other people, and more than 20% may take medications not currently prescribed and commit drug administration errors that could have serious clinical consequences (Lamy, Salzman, & Nevis-­Olesen, 1992). Similarly, inappropriate drug discontinuation may occur up to 40% of the time in this population (Jackson, Ramsdell, Renvall, Smart, & Ward, 1984). Older adults can experience age-­related declines in the cognitive processes necessary for successful medication adherence (Raz, 2000) and therefore may be at higher risk for neglecting to take medications as prescribed. This risk is accentuated for individuals suffering from chronic disease. One of the most prominent causes of nonadherence among older adults is forgetfulness related to medication administration (Col et al., 1990; Leirer, Morrow, Pariante, & Sheikh, 1988). In an adherence study

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among older adults, Col and colleagues (1990) reported that poor recall had a stronger relationship to treatment nonadherence than did any other predictor (odds ratio = 7.1). Memory failure leading to poor medication adherence likely takes two forms. As elaborated by Morell, Park, and Poon (1990), patients must (1) remember the correct way to take a medication (retrospective memory) and (2) must remember to do so at the proper time (prospective memory). Morell and colleagues have found that (1) older adults have poorer recall of drug instructions than do younger controls; (2) both younger and older individuals have more difficulty recalling medication regimens as they became more complex; and (3) even when given unlimited time to learn medication instructions, older adults often do not study drug instructions sufficiently well to recall them (i.e., they appear to be more prone to metamemory failures). Comprehension problems have also been shown to be associated with poor adherence to medication instructions among older adults, including comprehension of labels on pill bottles and instructions orally related by the patient’s physician (Diehl, Willis, & Schaie, 1995; Kendrick & Bayne, 1982). For example, Kendrick and Bayne (1982) reported that older adults had difficulty translating the instruction “Take every 6 hours” into a specific medication plan, and Hurd and Butkovich (1986) found that most older adults made errors when interpreting prescription labels. Similarly, Morrell, Park, and Poon (1989, 1990) found that about 25% of the information in a medication plan was misunderstood by older adults when they were presented with an array of prescription labels and asked to develop a medication schedule based on the instructions. Still other researchers have demonstrated that older adults have difficulty with comprehension of text when inferences are required (Cohen, 1981; Field, Mazor, Briesacher, Debellis, & Gurwitz, 2007; Metlay, 2008). Lower health literacy among older adults has also been implicated in poor comprehension of medication instructions and therefore poor overall adherence (Gazmararian et al., 2006; Morrow et al., 2006). These studies suggest that, as a result of age-­related declines in comprehension and memory, older adults tend to have less information available to them, relative to younger individuals, following exposure to medication information and drug administration instructions. In addition to age-­related decrements in memory and comprehension abilities, declines in sensorimotor function, attention, working memory, processing speed, and executive functioning have also been shown to be associated with adherence to medication regimens (Conn, Taylor, & Miller, 1994; Isaac & Tablyn, 1993). At a most basic cognitive level, impairments in sensorimotor function may lead to suboptimal medication adherence. For example, declines in perceptual acuity have been shown to interfere with patient discrimination of basic medication information such as medication tablet color (Hurd & Butkovich, 1986). Impaired motor function has also been shown to be related to problems with opening medication bottles and cutting pills (Isaac & Tablyn, 1993). With regard to attentional abilities, Zacks and Hasher (1997) have found that older adults are deficient in their ability to both direct and to inhibit their attention to irrelevant information. Similarly, older adults have been found to be highly susceptible to both internal and external distraction over delays (Hasher & Zacks, 1988; Rekkas, 2006). Such deficits become particularly problematic when older individuals are faced with the administration of multiple medications and complex drug regimens. Indeed, noncompliance has been shown to increase dramatically among older adults in relation to the number of drugs prescribed (Fernandez et al.,

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2006; Wandless & Davie, 1987). For example, dosage errors in one study increased 15-fold among older patients when the number of drugs prescribed was increased from one to four (Parkin, Henney, Quirk, & Crooks, 1986). Similarly, noncompliance was found to be 3.6 times more prevalent among older patients using two or more pharmacies to fill their prescriptions than among those using only one (Col et al., 1990). Medication adherence also involves working memory, processing speed, and numeric abilities. Working memory is the capacity to process, manipulate, and temporarily store new or recently accessed information (Salthouse & Babcock, 1991). Considerable empirical evidence has demonstrated that working memory functions also decline with age (Craik & Jennings, 1992; Park et al., 1996; Smith, 1996) and therefore negatively impact medication-­taking behaviors. In the context of self­medication management, individuals must keep the intention to take medicines in working memory while doing other things and must further rely upon these functions to integrate and develop a medication plan for following multiple drug regimens simultaneously. Salthouse (1996) and others have also demonstrated that older adults are slower at processing information in nearly all situations, relative to younger individuals. Processing speed has been defined as the speed at which mental operations are performed (Salthouse, 1996). Decrements in this domain are thought to compromise medication adherence behaviors by interfering with the complete processing and comprehension of information. For example, if mental operations regarding a medication regimen are performed too slowly, early information might be lost during the subsequent planning process. Finally, declines in numeric abilities, observed as early as age 50 (Schaie, 1996), are also hypothesized to inhibit correct dosage interpretation and to contribute to medication noncompliance. Although older adults may be more likely to have cognitive deficits that negatively impact adherence, there are, of course, many other factors that predict treatment compliance in this population. Factors contributing to increased medication adherence in this group may include greater stability in lifestyle, less drug and alcohol abuse, and greater familiarity with medication taking and the establishment of routines and regimens to do so successfully. Other predictors include financial status (i.e., can the older patient afford to buy medication?), untoward side effects (e.g., patients often unilaterally discontinue medications that produce intolerable side effects), health beliefs (e.g., increased internal locus of control and greater fatalism regarding health issues), and degree of social integration versus isolation. There is also compelling evidence to suggest that health literacy may play an important role in adherence behaviors among older adults. In fact, previous research has found health literacy to be quite poor in this group as a whole (Council on Scientific Affairs, 1999), and this limitation has significant effects on comprehension and adherence to medical instructions. For example, among 3,260 Medicare patients from a large managed care plan in four cities, 33% of English-­speaking and 54% of Spanish-­speaking respondents had inadequate to marginal health literacy (Gazmararian et al., 1999). Only 12% of respondents understood the correct timing of dosing medications, and only 16% understood how to take a medication on an empty stomach (Gazmararian et al., 1999). Baker, Parker, Williams, and Clark (1998) demonstrated that patients with inadequate health literacy were at a twofold greater risk of hospital admission, compared with patients with marginal or adequate health literacy.

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Research in the field of human factors (see Rogers et al., Chapter 2, this volume, for a comprehensive review), although not limited to older adult populations, has nevertheless contributed a wealth of additional information regarding ways to enhance the comprehension of complex medical information and improve medication compliance in this population. For example, studies have shown that both older and younger adults share preferences for the organization of medication information and tend to understand instructions in terms of what they already know about the task (Morrow, Leirer, Altieri, & Tanke, 1991; Morrow, Leirer, Andrassy, Tanke, & Stine-­Morrow, 1996). These preferences suggest a general schema that includes three categories: (1) general information (e.g., name and purpose of the medication), (2) how to take the medication (dose, time, duration, and warnings), and (3) possible outcomes of taking the medication (side effects and what to do in case of an emergency). Interestingly, instructions in these studies were recalled 13–20% more accurately when organized according to this preferred medication-­taking schema. Research in this area has also demonstrated that visual acuity and the presentation and style of written materials has a significant impact on individuals’ understanding and retention of information. Age-­related declines in visual functioning have been well documented and include reduced visual acuity, contrast sensitivity, diminished perception of peripheral targets, and poorer color discrimination (Kline & Scialfa, 1997). The perception of written information can be enhanced in those with vision problems by increasing contrast (especially for detailed stimuli), avoiding glare, avoiding subtle distinctions among colors, and minimizing the need to discriminate fine detail. Similarly, studies on the effects of aging suggest that larger font sizes (i.e., 12–14 points), conventional font styles, and use of unjustified text are more appropriate for older adults and those with vision impairments (Drummond, Drummond, & Dutton, 2004; Gregory & Poulton, 1970; Vanderplas & Vanderplas, 1980). Instructions can also be improved by adding icons that highlight important information (Wickens, 1992), as such images tend to be more explicit than text, reducing cognitive load and the need for inferential reasoning (Larkin & Simon, 1987). Other studies recommend using relatively short paragraphs, employing active rather than passive voice, avoiding double negatives, and clarifying the structure of text by using summaries, headings, and bullet points. Despite the wealth of information that has come from studies in the human factors literature, the health care industry has yet to implement many of these findings to enhance the comprehension of medical information among chronically ill patients. For example, as noted by Murray and colleagues (2004), the packaging and labeling of prescription medications has changed very little in the past 50 years; most patients filling prescriptions receive standard orange containers with minimal instructions printed in small font across a curved and glossy white sticker. Clearly, additional work is needed to translate current research findings into everyday, practical enhancements for medication comprehension and compliance.

Dementia Although considerably fewer in number, relative to studies involving normal older adults, investigations of medication adherence behavior in patients with dementia have recently begun to emerge in the literature. The exclusion of individuals with

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dementia in earlier studies presumably reflects a general assumption that the neurocognitive mechanisms underlying medication adherence in such individuals may be more obviously deteriorated than in those with other health conditions. Indeed, studies have confirmed that patients with dementing conditions such as Alzheimer’s disease typically have difficulty not only remembering which medications they are taking, but also the reason for their use, as a result of disruptions in short-term memory, judgment, and insight (Cooper et al., 2005). More interesting, however, are recent findings related to the association between frontal/executive deficits and poor medication adherence in this population. Results from such studies have implications for work with a variety of other populations, such those with traumatic brain injury and schizophrenia, for whom impairments in executive functions are common. Executive abilities are the basis for several cognitive processes, including planning, strategizing, maintaining focused attention, and task switching. Studies have found that executively impaired patients are more likely to resist care and are less likely to comply with medication regimens (Allen, Jain, Ragab, & Malik, 2003; Hinkin et al., 2002; Stewart, Gonzalez-Perez, Zhu, & Robinson, 1999). In one study, executive impairment explained 28% of the variance in the performance of activities of daily living in patients with Alzheimer’s disease (Boyle et al., 2003). Adhering to medication regimens requires the involvement of executive functions because taking medicines involves developing and implementing a consistent plan to adhere; remembering to adhere, which typically requires time-based (e.g., at 5:00 p.m.) or event-based (e.g., with food) prospective remembering; and remembering whether the medicine was taken as desired (described as “source monitoring”). Not surprisingly, prospective difficulties are associated with neurological compromise (e.g., HIV-1 infection, Woods et al., 2006; traumatic brain injury, Schmitter­Edgecombe & Wright, 2004). Also, the ability to monitor the source is likely to become more difficult when the action is repetitive (Einstein, McDaniel, Smith, & Shaw, 1998). Recall of an isolated event in this case (e.g., whether or not a dose of medication was taken before bed) can be hampered by the fact that similar events have occurred many times in the past and therefore, as a whole, tend to blur together in memory, due to repetition and reduction in novelty. Indeed, taking medicines for chronic conditions is often repetitive because the same medicine is taken in the same way day after day. Impaired executive functions may contribute to poor medication adherence in a number of ways. For example, individuals with deficits in such higher-order abilities may fail to take their medications because they cannot maintain the cognitive representation related to the need for medication in the face of other events. Similarly, persons with executive dysfunction may fail to organize their schedule in a manner necessary to accommodate medication taking. On the other hand, such individuals may perseverate on medication taking and unintentionally overdose. Moreover, executive deficits may contribute to faulty reasoning that medication adherence is not necessary or that alternative doses or regimens are acceptable. Additional studies have demonstrated that, in at least some cases, the functional loss associated with executive impairments may be behaviorally mediated by apathy. Indeed, apathy has been associated with executive impairments in patients with various forms of dementia (Royall, Chiodo, & Polk, 2000). For example, Boyle and colleagues (2003) demonstrated that executive impairment and apathy scores contrib-

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uted to 44% of the variance in instrumental activities of daily living in patients with Alzheimer’s disease. Given that executive functions are involved in behaviors associated with motivation, disruption of the neural circuitry maintaining these higherorder processes may lead to apathy and subsequent functional impairment, resistance to care, and impaired decision-­making capacity.

HIV/AIDS Our group has engaged in several longitudinal investigations designed to identify factors that are associated with medication adherence, with a particular emphasis on neurocognitive factors. Below we present an overview of the primary findings from these studies to illustrate how neurocognitive dysfunction can adversely affect medication adherence. The introduction of highly active antiretroviral therapy (HAART) has resulted in improved virological, immunological, and clinical outcomes, including improvement in neuropsychological functioning, in HIV-infected adults. Unfortunately, a number of studies has demonstrated that adherence rates of at least 90–95% are required for optimal viral suppression. Lower levels of adherence can lead to increased viral replication and the development of drug-­resistant HIV strains with obvious adverse personal and public health consequences. As previously discussed, cognitive compromise has been found to adversely impact various aspects of compliance with medical directives, including adherence. This relationship has been reported in HIV/AIDS as well as other chronic medical conditions such as diabetes and hypertension. Memory impairment, characterized especially by forgetfulness, motor and psychomotor slowing, attentional disruption, and executive system dysfunction have all been repeatedly observed among HIVpositive individuals. One recent study, funded by the National Institute of Mental Health (NIMH) and conducted by our group, involved 137 HIV-infected adults who completed a comprehensive battery of neuropsychological tests assessing learning and memory, speed of information processing, attention/working memory, verbal fluency, executive function, and motor speed (Hinkin et al., 2002). Using a methodology employed by Heaton and colleagues at the University of California at San Diego (UCSD) HIV Neurobehavioral Research Center (HNRC), test scores were converted to demographically corrected T-scores and grouped by cognitive domain. A global T-score was also calculated. A liberal cutpoint of one standard deviation below the mean (e.g., T < 40) was used to classify participants as cognitively compromised, because this technique allows for an appropriate balance between sensitivity and specificity, particularly in milder disorders. Medication adherence was determined using MEMS caps to measure HAART adherence over a 1-month time frame. Participants who took at least 95% of their prescribed doses were classified as “good adherers.” The mean adherence rate across all 137 participants was 80.2%. Only 34% (46/137) of participants were classified as good adherers, whereas two-­thirds of subjects were unable to adequately adhere to their medication regimen. Analyses examining the impact of cognitive compromise on adherence yielded the expected pattern of results. The cognitively impaired subjects’ mean adherence rate was only 70%, whereas the neuropsychologically normal subjects evidenced a mean adherence rate

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of 82%. Logistic regression analyses revealed that neuropsychologically compromised individuals were twice as likely to be classified as poor adherers. Finer-­grained examination of the neuropsychological test data revealed that it was executive dysfunction and working memory impairment that drove this relationship. Contrary to our expectations, learning and memory were not significantly associated with medication adherence.

Neuropsychological Dysfunction, Regimen Complexity, and Medication Adherence Although considerable progress has been made in simplifying HIV medication regimens, historically the effective pharmacological management of HIV/AIDS has involved strict adherence to an extremely demanding, often complex, medication regimen (upwards of 20–30 pills/day, many having specific, compound instructions, such as “Take three times per day on an empty stomach”). Clearly, the relationship between neurocognitive integrity and medication adherence may be mediated by regimen complexity. Medication adherence may then be particularly problematic for the cognitively compromised patient who is on a more complex medication regimen. Using the above data set, we explored the relationship between neuropsychological dysfunction, regimen complexity, and adherence. As can be seen in Figure 6.1, not only does regimen complexity (defined as a three-times-daily schedule) adversely affect medication adherence, but this effect is particularly pronounced among the cognitively impaired, who were able to successfully adhere to only a little over half of their prescribed doses. Complex medication regimens were not nearly as problematic for the neuropsychologically normal participants. 100 One or two doses/day Three doses/day

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FIGURE 6.1.  Relationship between cognitive status, regimen complexity, and medication adherence among HIV-infected adults.  = one or two doses per day; = three doses per day. From Hinkin et al. (2002). Reprinted with permission from Lippincott Williams & Wilkins.

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Aging, Neuropsychological Impairment, and Adherence A number of studies has found older HIV-infected adults to be at greater risk for neuropsychological compromise. Because of this heightened risk of cognitive impairment, we posited that older subjects (defined here as over the age of 50) would be less adherent than younger subjects. Contrary to our expectations, we found that the older subjects were actually far more adherent than younger subjects. As can be seen in Figure 6.2, 53% of older subjects were classified as good adherers, whereas only 26% of younger subjects were able to attain a 95% adherence rate. Using a more liberal 90% cutpoint to define good adherence, 71% of older subjects were found to be adherent versus only 37% of younger subjects. It may be that taking medication requires less alteration in lifestyle for older adults or that such alterations are less burdensome for older individuals who may more easily accommodate pill taking into their daily activities. Older adults are also more likely to have prior experiences taking daily medications for other age-­related illnesses. However, a different picture emerges when we look at the interaction between advancing age and neurocognitive compromise. We first grouped the above subjects as a function of their medication adherence (using the 95% adherence cutpoint to dichotomize subjects) and age (using age 50 as a cutpoint) and then compared these groups’ performances on neuropsychological testing. For illustrative purposes, Figure 6.3 depicts performance on the Trail Making Test and the California Verbal Learning Test (CVLT). As shown in this figure, there was little difference in cognitive functioning between the two younger groups and the older good adherers. In decided contrast, the older subjects who were poor adherers performed far worse on neuropsychological testing. These findings suggest that the concomitant presence of both advancing age and neurocognitive impairment poses particular challenges regarding medication management. While we have conceptualized cognitive dysfunction as causing poor adherence, it is equally plausible that poor adherence results in a number of untoward

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FIGURE 6.2.  Medication adherence in younger (< 50 years) and older (≥ 50 years) HIV-infected adults.  = good adherence (≥ 95%);  = poor adherence (< 95%); Older = age 50 or older; Young = less than age 50. From Hinkin et al. (2004). Reprinted with permission from Lippincott Williams & Wilkins.

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FIGURE 6.3.  Trail Making and CVLT performances for younger and older HIV-infected adults as a function of medication adherence. Older = age 50 or older; Younger = less than age 50; CVLT = California Verbal Learning Test.

clinical outcomes, including neuropsychological impairment. In all likelihood, a bidirectional relationship exists, with cognitive impairment adversely affecting patients’ ability to adhere to their medication regimen, which in turn results in further disease progression and a worsening of cognitive function. Figure 6.4 depicts the relationship between medication adherence and specific neurocognitive domains among older HIV-infected patients. In addition to higher rates of global neurocognitive impairment, poor adherence was associated with compromised executive function, memory, and speed of information processing.

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FIGURE 6.4.  Medication adherence in older HIV-infected adults as a function of neurocognitive impairment.  = good adherence (percentage adherence ≥ 95%);  = poor adherence (percentage adherence < 95%); Imp = cognitively impaired; Norm = cognitively normal; Global = a composite of all the individual cognitive domains; * = significant at p < .05. From Hinkin et al. (2004). Reprinted with permission from Lippincott Williams & Wilkins.

Drug Use/Abuse and Medication Adherence Drug use or abuse may also adversely affect medication adherence via several potential mechanisms. Multiple studies have found substance abuse to be a risk factor for the development of neuropsychological impairment. Drug use can also give rise to new-onset psychiatric dysfunction or exacerbate a preexisting condition. Disruptions to sleep and eating patterns and increased psychosocial instability may also contribute to poorer adherence. In a longitudinal study funded by the National Institute on Drug Abuse (NIDA) and based on a different cohort, we examined the impact of drug use and abuse on medication adherence among 150 HIV-infected individuals, 102 of whom tested urinalysis positive for recent illicit drug use. Medication adherence was tracked over a 6-month period using an electronic monitoring device (MEMS caps). We found that individuals urine-­positive for illicit/recreational drugs demonstrated significantly worse medication adherence than did drug-­negative participants (63 vs. 79%, respectively). Logistic regression revealed that drug use was associated with over a fourfold greater risk of adherence failure. The use of stimulants (i.e., cocaine or methamphetamine) proved to be particularly disruptive to adherence in this sample of HIVinfected adults. Participants who tested positive for stimulants were seven times more likely to be poor adherers than those who tested negative. Interestingly, we were able to compare adherence rates for time periods when subjects were not using stimulants to time periods when those same subjects were using stimulants. We computed 3-day adherence rates for visits at which participants tested stimulant positive as well as adherence rates for visits at which the same participants tested stimulant negative. For each participant this yielded two adherence rates corresponding to when he or she was and was not using stimulant drugs. The 3-day mean adherence rate for participants who tested positive for recent stimulant

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use was 51.3% compared to a 3-day mean adherence rate of 71.7% for the same participants when they had not recently used stimulants. As such, the deleterious impact of drug use on adherence appears to be more a function of state rather than trait. This finding suggests that it is the acute effects of intoxication, rather than stable features that may be characteristic of the drug-using populace, which adversely affect medication adherence.

Psychiatric Status Studies have reported nonadherence rates among psychiatric patients ranging from 26% (Drake, Osher, & Wallach, 1989) to as high as 73% (Razali & Yahya, 1995), depending on patient characteristics and the technique used to measure medication adherence. In unipolar disorders the 1-year relapse rates are as high as 80% in patients not taking antidepressants, as compared to 30% for those who adhere, yet 60% of patients discontinue their medications within 3 months of beginning treatment (Myers & Brainthwaite, 1992). Similarly, approximately 60% of patients admitted with mania failed to adhere to their medication regimen in the month prior to hospitalization (Keck et al., 1996). Psychiatric disturbances, including depression and anxiety, have been associated with poorer medication compliance, to varying degrees, in studies using diverse measures and methodologies (Carney, Freedland, Eisen, Rich, & Jaffe, 1995; Edinger, Carwille, Miller, Hope, & Mayti, 1994; Hinkin et al., 2000; Sensky, Leger, & Gilmour, 1996; Shapiro et al., 1995). For example, a small meta-­analysis (12 articles) found that depressed patients were three times more likely to be noncompliant with medication and behavioral treatment regimens (Dimatteo, Lepper, & Croghan, 2000). Similarly, patients suffering from coronary artery disease who also had a diagnosis of major depression adhered to their cardiac medication regimen less than 45% of the time during a 3-week adherence monitoring phase, whereas nondepressed patients showed 70% adherence (Carney et al., 1995). Shapiro and colleagues (1995) found psychiatric disturbance to be one of the best predictors of decreased compliance and mortality following heart transplantation. Likewise, Wang and colleagues (2002) demonstrated significant effects of depressive symptomatology on antihypertensive medication regimens, and Ciechanowski, Katon, and Russo (2000) found that depressive symptom severity was associated with poorer diet and medication regimen adherence among diabetics. A study by Sensky and colleagues (1996) found that affective disturbance interacted with other psychosocial variables, including health locus of control and social support, to negatively impact adherence to diet and other health behaviors among patients on chronic hemodialysis. Finally, neuropsychiatric dysfunction, including apathy, depression, and hostility, has been related to decreased adherence among older adults as well (Carney et al., 1995). Recent studies have revealed that cognitive deficits associated with psychiatric illness may play a role in adherence behaviors. For example, there is increasing evidence that neurocognitive impairment in some patients with bipolar disorder is enduring and may represent a trait rather than a state variable. Deficits in learning and memory (Cavanagh, Van Beck, Muir, & Blackwood, 2002) and executive abilities (Dupont et al., 1990; Goldberg et al., 1993; Morice, 1990) have been found consistently, even during the euthymic phase of the illness. Such impairments may

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have detrimental effects on patients’ ability to remember dosing instructions and to appropriately plan and organize a medication-­taking regimen. In addition to the neurocognitive deficits associated with psychiatric illness, a variety of other factors, such as ethnicity and attitudes and beliefs about psychotropic medication, may play a role in determining adherence behaviors among those with emotional disorders. For example, 78% of 2,000 people surveyed in one study thought that antidepressants were addictive, less than half thought they were effective, and only 16% believed that they should be given to individuals with depression (Priest, Vize, Roberts, Roberts, & Tylee, 1996). These findings suggest that physicians and others mental health practitioners have an important role in educating patients and the general public about the safety and efficacy of available antidepressant agents. With regard to ethnicity, a number of studies has suggested that patients from ethnic/minority groups are less likely to adhere to psychotropic medications than nonminority patients (Fleck, Hendricks, Del Bello, & Strakowski, 2002; Sleath, Rubin, & Huston, 2003). Specifically, previous research has found that both Hispanic and African American primary care patients may be less receptive to antidepressant medications than European Americans (Cooper et al., 2003). Poor adherence in these groups may be due to prohibitive cost, beliefs about depression or other psychiatric illnesses and the importance of its treatment, concerns about antidepressants, treatment preferences, and stigma related to taking psychiatric medication (Bultman & Svarstad, 2000; Maidment, Livingston, & Katona, 2002; Sirey et al., 2001). The studies reviewed above help to explain medication-­taking behaviors in some individuals; factors associated with adherence to medication regimens have also been studied in the context of more severe psychopathology, including those with schizophrenia and other psychotic disorders. Despite the overwhelming evidence that neuroleptic medication is effective in the treatment of schizophrenia, many patients do not take their medications (Dencker & Liberman, 1995; Hale, 1995), and antipsychotic nonadherence is therefore a major barrier to the effective pharmacological treatment of these individuals (Dolder et al., 2004). Several studies have shown that approximately one-third of patients with schizophrenia are fully compliant with medications, one-third are partially compliant, and one-third of patients are entirely noncompliant (Buchanan, 1992; Fleischhacker, Meise, Gunther, & Kurz, 1994; Weiden, Shaw, & Mann, 1995). Moreover, 55% of people with schizophrenia who do not take antipsychotic medication will relapse over the course of a year, compared to only 14% of those who comply with their medication regimen (Stephenson, Rowe, Haynes, Macharia, & Leon, 1993). Not surprisingly, multiple investigations have demonstrated that poor medication adherence among patients with schizophrenia is associated with a variety of poor health outcomes, including increased rehospitalization, repeated emergency room visits, worsening of symptoms, and even homelessness (Marder, 1998; Moore, Sellwood, & Stirling, 2000; Olfson et al., 2000; Weiden & Olfson, 1995). Several reviews of the literature on medication adherence in schizophrenia (Fenton, Blyler, & Heinssen, 1997; Kampman & Lehtinen, 1999) have identified consistent predictors of poor adherence in this population, including more severe psychopathology, comorbid substance abuse, presence of medication side effects, depressive symptoms, an absence of social support from family or friends, practical barriers (e.g., inability to afford medications), lack of insight, and neurocognitive dysfunction.

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Although there is substantial heterogeneity among such individuals with regard to the level and pattern of cognitive impairment, some of the most commonly impaired abilities in schizophrenia include attention, working memory, verbal and nonverbal learning, executive skills, and some psychomotor abilities (Heaton & Drexler, 1987; Heinrichs & Zakzanis, 1998; Schwartz, Rosse, Veazey, & Deutsch, 1996). A major barrier to adherence in this population is related to a lack of insight or an inability to understand one’s disorder and the need for treatment (Lacro, Dunn, Dolder, Leckband, & Jeste, 2002). Diminished insight, coupled with other cognitive deficits, may decrease patients’ ability to adhere to their treatment regimens (Green, 1996; Green, Kern, Braff, & Mintz, 2000).

Psychosocial Models of Adherence Although the primary thrust of this chapter has been to highlight the relationship between cognitive abilities and medication adherence, many other intra- and extra­ personal factors have been shown to have an impact on adherence behaviors. Qualitative studies have indicated that medication adherence is dynamic and multifactorial in determination (Remien et al., 2003). Several of these studies suggest that medication adherence is influenced by side effects, self-­efficacy, lifestyle factors and self­identity, illness ideology, affect, and medication burden (Carrick, Mitchell, Powell, & Lloyd, 2004; Remien et al., 2003; Wilson, Hutchinson, & Holzemer, 2002). Numerous theories developed to explain health-­related behaviors have been applied to the study of medication adherence. A detailed review of this work is beyond the scope of this chapter, but, in general, most of these theories deal with external influences (e.g., environmental, social) and/or internal influences (e.g., attitudes/beliefs, motivation) on medication-­taking behavior. One such internal influence is self-­awareness of bodily sensations and processes. Interestingly, in a sample of patients in hemodialysis (N = 52), bodily self-­focusing tendencies (i.e., increased attention to physical sensations) were shown to interact with illness-­related physical impairment to influence medication and dietary adherence (Christensen, Wiebe, Edwards, Michels, & Lawton, 1996). High body self-­focusing and illness-­related physical impairments were predictive of poorer adherence, whereas high body self-­focusing and low illness­related physical impairments predicted better adherence. These findings suggest an interaction between treatment expectancies (based on illness-­related impairment) and body self-­focusing in which adherence behavior improves with positive expectancies and high body self-­focusing and declines with negative expectancies and high body self-­focusing. However, treatment expectancies are likely multifactorial in nature, reflecting current and past health status, real and perceived barriers to adherence, self-­efficacy, and motivation (Christensen et al., 1996; Reynolds, 2003; Williams, Rodin, Ryan, Grolnick, & Deci, 1998).

Health Beliefs Model In order to better predict complex health-­related behaviors such as medication compliance, the health beliefs model was proposed (HBM; Rosenstock, 1974). The HBM is derived from a well-­established body of psychological and behavioral theory and

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posits that health behaviors depend mainly on the desire to avoid illness and the belief that certain actions will prevent or alleviate disease. The model consists of a number of dimensions, including perceived susceptibility to illness, perceived illness severity, perceived benefits of treatment, and perceived barriers to treatment compliance. “Perceived susceptibility” refers to an individual’s belief that he or she is at risk of contracting an illness or, in the case of a previously established infection, belief in the diagnosis and the vulnerability to illness in general. “Perceived illness severity” describes feelings regarding the seriousness of contracting an illness or of leaving it untreated, and requires evaluation of possible social, occupational, and psychological consequences. “Perceived benefits of treatment” refers to beliefs in the effectiveness of various actions in reducing disease threat. Finally, “perceived barriers to treatment compliance” describes a kind of cost–­benefit analysis in which individuals weigh the treatment’s effectiveness against potential negative consequences of compliance, such as disruption of daily activities and adverse side effects. HBM theory predicts that individuals are more likely to comply with treatment regimens if they perceive themselves to be potentially vulnerable to the illness, perceive the consequences of illness as severe, are convinced of the efficacy of the proposed treatment regimen, and see relatively few costs associated with adherence (Budd, Hughes, & Smith, 1996; Smith, Ley, Seale, & Shaw, 1987). In addition to these four dimensions, the HBM also postulates that diverse demographic, psychosocial, and psychological variables may affect individuals’ perceptions and thereby indirectly influence health-­related behaviors. The model also states that individuals need a prompt (e.g., a reminder either of the threat of illness or the action that must be taken against it) before they will engage in health-­related behaviors (Weinstein, 1988). These “cues to action” may be internal (e.g., recognition of prodromal symptoms) or external (e.g., comments made by significant others). The HBM has been shown to explain variation in medical regimen adherence behavior in patients with a variety of diseases and disorders, including HIV (Barclay et al., 2007), hypertension (Brown & Segal, 1996; Mendoza, Munoz, Merino, & Barriga, 2006), diabetes (Harris, Skyler, Linn, et al., 1982), heart disease (Mirotznik, Feldman, & Stein, 1995), epilepsy (Green & Simons-­Morton, 1988), end-stage renal disease (Cummings, Becker, Kirscht, & Levin, 1982), and psychiatric illnesses such as depression and schizophrenia (Adams & Scott, 2000; Cohen, Parikh, & Kennedy, 2000).

Autonomy and Self-­Efficacy Others have attempted to explain medication adherence via social determination frameworks in which autonomous (volitional) and controlled (nonvolitional) behavior regulation are distinguished. Whereas “self-­efficacy” refers to one’s belief in one’s own ability to organize and execute an action, “autonomy” relates to one’s regulation of actions. In one study, a mixed sample of patients (N = 126) that was required to adhere to simple medication regimes (M = 1.36 medications, M = 1.40 doses daily), a sense of autonomy with regard to health care management accounted for 68% of the variance in medication adherence (Williams et al., 1998). Furthermore, perceived physician support of autonomous health care management was found to significantly mediate this relationship. Interestingly, in this study, perceived barriers did not predict

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adherence, although they were negatively correlated with autonomy and perceived autonomy support. The authors suggest that autonomy mediates the relationship between perceived barriers and adherence, such that individuals with higher levels of health care autonomy and perceived support for health care autonomy perceive fewer barriers to adherence. It is also possible that increased autonomy facilitates self­efficacy, thereby reducing perceived barriers. Whatever the case may be, this study highlights the role of personal control and social support as important contributors to medication adherence behaviors.

Theory of Reasoned Action A related psychological model for explaining health-­related behaviors is the theory of reasoned action (Ajzen & Fishbein, 1980). The central tenet of this model is that the intention to adhere is the best predictor of ultimate adherence, with intentions seen as a function of patients’ beliefs and expectations, their values, and the normative pressures exerted by their social referent group. Noting that the best intentions are often thwarted if the requisite abilities or opportunities are lacking, Ajzen (1985), in his theory of planned behavior, incorporated locus of control into the previous model. Research has shown both models to have reasonable predictive utility, with the theory of planned behavior more appropriate for health care situations not entirely under the patient’s perceived control (Millstein, 1996).

Social Action Theory Social action theory (SAT; Ewart, 1991) was formulated to deal with interactions between internal and external factors. SAT holds that contextual influences (physical environment, social/cultural environment, and biological factors) interact and modulate mood and arousal, which impact self-­regulatory processes (motivation, problem solving, generative capabilities, and interactive social factors) that, in turn, lead to action states (e.g., medication adherence). A large (N= 2,765), multisite (Los Angeles, Milwaukee, New York City, San Francisco) study of HIV-positive individuals on complex antiretroviral therapy (ART) revealed that the contextual factors of African American heritage, number of daily doses, symptom difficulty, being in a primary relationship, having a history of drug use (injection drug use or crack cocaine use), and having a history of homelessness were predictive of poor (< 90%) adherence (Johnson et al., 2003). Additionally, poor adherers also reported higher rates of depressive, anxious, and stress-­related symptoms. Self-­regulatory factors associated with poor adherence were low adherence self-­efficacy, difficulty fitting medication schedule into daily routines, problems managing medication side effects, fatigue related to medication adherence, and disbelief in the efficacy of the prescribed ART regimen. The positive association between being in a primary romantic relationship and nonadherence was surprising. The authors addressed this finding by suggesting that the majority of their participants may have been involved in problematic relationships, although this was not assessed. Others have demonstrated that negative relationships can have a deleterious effect on medication adherence (Dimatteo, 2004; Perlick et al., 2004). That said, Johnson and colleagues’ (2003) study supports an

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association between medication adherence and contextual and self-­regulatory factors. Future studies investigating these relationships with structural equation modeling may help to better test the predicted interactions posited by SAT.

Additional Psychosocial Models One contextual factor that has received considerable research attention to date is social support, which is often differentiated into structural and functional components (Berk, Berk, & Castle, 2004; Dimatteo, 2004; Simoni, Frick, & Huang, 2006; Weaver et al., 2005). Structural components are variables such as marital status, living arrangement, and social network density, whereas functional components describe variables such as practical support, emotional support, and family cohesion. A recent meta-­analysis on social support and medical regimen adherence demonstrated that several areas of functional support were highly associated with medical adherence (Dimatteo, 2004), the strongest of these being practical support. Emotional support and family cohesiveness were also associated with adherence, and family conflict was negatively related to medication adherence. Similarly, others have shown that medication adherence suffers when caregivers are overburdened (Perlick et al., 2004). Among structural supports in Dimatteo’s meta-­analysis, marital status and living with someone were modestly related to adherence. Overall, this meta-­analytic review indicates the importance of social support, particularly functional social support, in medical regimen adherence, although it does not specify how this relationship may be moderated. Recent studies of ART adherence in HIV-positive individuals have attempted to delineate the nature of the relationships between social support, affect, and medication adherence, using structural equation modeling. One such study (Simoni et al., 2006) showed that social support was positively associated with spirituality and negatively associated with negative affect (e.g., depression), which, in turn, predicted adherence self-­efficacy in HIV-positive individuals (N = 136). Adherence self-­efficacy was predictive of self-­reported adherence, which, in turn, predicted viral load. Unfortunately, the model only accounted for 8% of the total variance in ART adherence. A more compelling study, also utilizing structural equation modeling, demonstrated that the relationship between medication adherence, social support, and affect was mediated by avoidant coping strategies reported by HIV-infected individuals (N = 322; Weaver et al., 2005). More specifically, 20% of the variance in medication adherence (measured via MEMS caps) was predicted by negative affect and poor social support, as moderated by avoidant coping. The same model also accounted for 44% of the variance in viral load. In sum, many theories have been postulated to explain medication adherence behaviors across a wide range of patient populations. Research generated by these theories has demonstrated that sociodemographic factors (e.g., homelessness, drug use, ethnic/racial minority status), treatment expectancies, health beliefs/attitudes, self-­efficacy, a sense of autonomy in health care management, social support, cohesive and positive support networks, emotional functioning, and coping styles seem to play a part in medication adherence. Additionally, many researchers have begun to examine how these factors interact and influence adherence, although the many complexities in this area of investigation have limited our understanding of medication-­taking

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behavior. Moreover, little is known about the interaction between these numerous factors and neurocognitive deficits in attention/concentration, memory, or executive functioning. Beyond their direct effects on medication adherence, such cognitive difficulties may moderate or mediate the influence of predictors such as treatment expectancies, health beliefs, autonomous health care management, coping styles, and social support. Indeed, recent data collected from individuals suffering from schizophrenia have shown that difficulties in sustained attention, verbal memory, and executive functioning correlate with their attitudes and beliefs about medications (Kim et al., 2006; Maeda et al., 2006).

Medication Adherence Interventions Given the prevalence of suboptimal medication adherence across patients with chronic illnesses and neurocognitive deficits, interventions aimed at improving medication­taking behavior are clearly needed. A wide variety of strategies has been used to increase compliance in such populations, including rehabilitation techniques aimed at improving actual neurocognitive impairments, as well as compensatory mechanisms designed to support limitations in cognitive functioning, such as pillboxes, medication charts, and voice-mail reminder services. Intervention strategies used to improve medication management in populations with neuropsychological deficits have not been thoroughly investigated to date. Although interventions that do not specifically target cognitive abilities have been shown to improve medication adherence in some individuals with such deficits (e.g., Antoni et al., 2006; Higgins, Livingston, & Katona, 2004), cognitive impairments have nevertheless been demonstrated to interfere with medication adherence in many populations (Chen et al., 2005; Heaton et al., 2004; Park, Morrell, Frieske, & Kincaid, 1992). In the past, these deficits have been treated with restorative (e.g., practice drills to improve memory function) or compensatory interventions (e.g., use of a daily planner to improve memory for daily events) (Wilson, 1999). Restorative techniques are generally used during periods of natural recovery (e.g., in the first 6 months following a traumatic brain injury) but are typically discontinued when improvements in function begin to plateau (Wilson, 2000). Rehabilitation strategies used to target attention, memory, and executive functioning impairments have been used on an individual basis and in combination. Targeting isolated cognitive deficits is obviously most beneficial for individuals who suffer from solitary impairments. Whereas targeting cognitive impairments separately may also enhance medication-­taking behaviors in persons with multiple/overlapping cognitive deficits, intervention strategies that approach medication adherence as a more complex neurocognitive operation will likely result in more significant and tangible outcomes. In addition to cognitive remediation techniques, researchers have evaluated the effectiveness of external aids such as pillboxes and pill bottle alarms (Mackowiak et al., 1994; Park et al., 1992), voice-mail reminders (Andrade et al., 2005; Leirer, Morrow, Tanke, & Pariente, 1991), and organizational charts (Park et al., 1992). Haynes and colleagues have conducted several reviews of medication intervention research (Haynes, McKibbon, & Kanani, 1996; Haynes et al., 2000, 2003) and have con-

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sistently observed that successful interventions are often complex and involve some combination of providing more convenient care, education, and reminders to take medication, as well as encouragement to self-­monitor and increased support through counseling, family therapy, and/or supervision. However, even the most effective interventions have not led to notable outcomes. In fact, for the most part, adherence interventions have been found to be minimally effective; that is, effect sizes have typically been small (≤ .25), indicating that detecting significant change with such strategies would be difficult (Haynes et al., 2003). Despite limitations in overall effectiveness, a few studies have shown increased adherence with the use of various compensatory strategies. For example, Park and colleagues (1992) found that older adults who used both an organizer and a chart designed to minimize cognitive effort in taking medications made considerably fewer errors in their medication regimens than control subjects (18.3% for controls vs. 1.8% for the intervention group). The use of charts and written instructions to augment verbal communication has also been shown to be beneficial in a number of other studies (Coe, Prendergast, & Psathas, 1984; Lamy et al., 1992). Similarly, color-coded pill bottles matched with a weekly pillbox have been demonstrated to enhance compliance in some patients (Martin & Mead, 1982). Finally, Leirer and colleagues (1991) found a fourfold reduction in episodes of poor adherence when voice-mail reminders were used to cue older subjects about their medication regimen. Well-­designed medication instructions that reduce cognitive demands and motivate the patient may also work to improve adherence (Park, Willis, Morrow, Diehl, & Gaines, 1994). Such instructions may be even more effective if, in addition to explaining how to take medication, they present information that targets incorrect beliefs about the illness or the drug. For example, Carter, Beach, and Inui (1986) found that reminder messages for flu vaccinations improved clinic attendance significantly when they contained information that addressed incorrect beliefs about the side effects and risks of the immunizations. Some experts have noted that there is a number of interventions that physicians and other health care providers could perform more regularly to potentially increase medication adherence in their patients. These interventions include explaining why particular medicines are being prescribed and what outcome is expected, emphasizing the shared responsibility between physician and patient, and trying to anticipate common barriers to adherence (e.g., economic limitations, cognitive impairment, negative side effects). Other important interventions include providing information about health conditions and medications both orally and in writing at a level understandable to patients, asking patients to explain the consequences of not taking medications or how to cope with adverse side effects, and mobilizing patients’ families to assist in providing additional support or supervision of their family members’ medication taking. Conducting regular medication reviews has also been associated with improved adherence in some patients. Such reviews may enhance adherence by improving the doctor–­patient relationship and/or by emphasizing the relevance and importance of medications. Given the frequency of polypharmacy, particularly in older populations, reviewing and possibly reducing the number of medications may also help to improve adherence. Some researchers have also suggested that the timing of medication taking should be matched to patients’ daily schedules when fea-

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sible (Coe et al., 1984), because if regimens interfere with normal everyday activities, poor adherence is more likely to occur. Finally, improved detection and treatment of dementia, psychiatric disorders, and alcohol use disorders may also have a positive impact on adherence. As noted earlier, the field of human factors has the potential to play a major role in the improvement of medication adherence across populations and disease treatment models. Research in this area has demonstrated that the presentation and style of written materials have a significant impact on individuals’ understanding and retention of information. Given that both older and younger adults share a preference for medication information presented in an organized format (Morrow et al., 1991, 1996), adherence may be improved if drug instructions are delivered in a way that more closely conforms to this desired schema. For example, studies suggest that use of larger font sizes, conventional font styles, and unjustified text may be more appropriate for older adults and those with vision or cognitive impairments (Drummond et al., 2004; Vanderplas & Vanderplas, 1980). Instructions may also be improved by adding icons that highlight important information (Wickens, 1992), using relatively short paragraphs, and relying more heavily upon summaries, headings, and bullet points.

Future Directions Most early research on medication-­taking behaviors focused on single constructs as possible determinants of poor adherence, such as the demographic features of the patient (e.g., age, race, gender) or aspects of the therapeutic regimen (e.g., number of medications). However, as illustrated in this chapter, medication adherence is an extremely complex behavior, and it is likely that no single variable can account for the rates of poor compliance that have been observed across various diseases and patient populations. Therefore, more research that considers several of these explanatory factors simultaneously is greatly needed. Only by examining complex models of adherence behavior that take into account demographic, medication, disease, psychosocial, and neurocognitive variables will the most important predictors of adherence begin to be identified. Considering the limited effectiveness of many available medication adherence interventions, greater emphasis should be placed on finding effective techniques to improve medication compliance and clinical outcomes. Interventions are needed to enhance patient education, increase patients’ health literacy, encourage the use of drug delivery systems, improve monitoring of medication use, and enhance communication among providers about patients’ adherence patterns. Because the factors influencing adherence are many and varied, multifaceted, tailored interventions may be necessary to improve self-­administration of medications in most populations. Finally, because physicians frequently underestimate subtle impairment or disability in their patients (Calkins et al., 1991; Canadian Task Force, 1991), clear-cut practice guidelines and suitable methods of measuring those cognitive, motor, and sensory functions required for accurate drug administration are essential for ultimate preventive management.

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Acknowledgments This chapter includes data gathered as part of a National Institute of Mental Health (NIMH)– funded study (No. RO1 MH58552) and a National Institute on Drug Abuse (NIDA)–funded study (No. RO1 DA13799). Support was also provided by the VA Merit Review program. Terry R. Barclay and Matthew J. Wright were supported by an NIMH training grant (No. T32 MH19535).

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Chapter 7

The Brain on the Road Matthew Rizzo and Ida L. Kellison

A

utomobile driving has become an indispensable activity of daily life, yet vehicular crashes injure millions and regularly kill over 40,000 people in the United States each year at a cost of about $230 billion dollars (National Highway Traffic Safety Administration [NHTSA], 2003). About 1.2 million people die worldwide due to vehicular crashes and tens of millions are injured (Peden & Sminkey, 2004). This is a preventable global disaster that must be urgently addressed. This chapter examines relationships between cognition and driver safety and tools for discriminating between safe and unsafe drivers. Studies of normal and cognitively impaired operators in controlled circumstances in a driving simulator and in the field (i.e., in natural and naturalistic settings using instrumented vehicles) reveal valuable information on the coordinated activities of neural systems (e.g., attentional, visuomotor, and decision making) required to safely drive a car. The results can inform public policy and help guide the development of in-­vehicle safety countermeasures to avert real-world car crashes, injuries, and death. A host of medical, neurological, and psychiatric disorders (e.g., Alzheimer’s disease, Parkinson’s disease, sleep disorders, personality disorders, and effects of licit and illicit drugs) can impair the ability to drive. The purpose of this chapter is to outline general principles for approaching these problems rather than to detail a specific approach to each disorder.

Conceptual Framework The chain of causality in vehicular crashes can be conceived as follows: Cognitive abilities and impairments determine specific driver behaviors and safety errors, which in turn predict crashes. In many cases, the causal pathway involves a concatenation of factors or events, some of which can be prevented or controlled (Runyan, 1998). Interventions for injury prevention and control can operate before, during, or 168

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after a crash occurs at the levels of driver capacity, vehicular and road design, and public policy (Haddon, 1972; Michon, 1979). Relationships between driver performance factors and safety errors can be represented by an imaginary triangle (Heinrich, Petersen, & Roos, 1980) or “iceberg” (Maycock, 1997). Visible above the “water line” are safety errors that produce car crashes resulting in fatality, serious injury, mild injury, or (most frequently) only property damage. Submerged below the water line are behaviors that are perhaps more indirectly related to crashes and occur more frequently. These range from relatively innocuous errors, such as failing to adjust a seat belt or check the rearview mirror on a deserted highway, to more serious errors such as choosing to drive while drowsy or distracted. These errors lead to deviations into opposing traffic lanes and produce near crashes (a.k.a., “near misses”). Although crashes produce an overwhelming public health burden, they are statistically infrequent events and tend to follow a Poisson distribution (i.e., a discrete probability distribution expressing the probability of a number of events occurring in a fixed period of time if these events occur with a known average rate and are independent of the time since the last event; e.g., Siskind, 1996; Thomas, 1996). A key strategy for research on determining crash risk in the real world is to discover the relationships between high-­frequency–low-­severity events that produce errors or near misses but not crashes, and the low-­frequency–high-­severity events that lead to reported crashes in states’ epidemiological records. It is also important to understand how mental mechanisms and vehicular and road system design features underpin cognitive errors in real-world tasks. The driving task may involve “hidden” strategic, tactical, and operational variables that are simply not tapped by standard neuropsychological probes (Michon, 1979). Figure 7.1 depicts a simple information-­processing model for understanding driver errors that may lead to vehicular crashes and shows where different impairments may interrupt different stages in the model. The driver (1) perceives and attends to stimulus evidence (e.g., through vision, audition, vestibular, and somatosensory inputs) and interprets the situation on the road; (2) formulates a plan based on the particular driving situation and relevant previous experience or memory; (3) executes an action Disorders of vision and attentional processing

Stimulus

Perceive, attend, and interpret the stimulus

Executive dysfunction

Motor disorders

Plan action (select response)

Execute action (implement response)

Previous experience (memory)

Memory disorders

Behavior

Feedback

FIGURE 7.1.  Information-processing model for understanding driver error.

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(e.g., by applying the accelerator, brake, or steering controls); and (4) monitors the outcome of the behavior as a source of potential feedback for subsequent corrective actions. The driver’s behavior is either safe or unsafe as a result of errors at one or more of these stages in the driving task. The risk of driver errors increases with deficits in attention, perception, response selection (which depends on memory and decision making), response implementation (a.k.a., executive functions), and awareness of cognitive and behavioral performance (a.k.a., metacognition). As we shall see, the individual’s emotional state, level of arousal (or sleepiness), psychomotor factors, and general mobility (e.g., Marottoli, Cooney, Wagner, Doucette, & Tinetti, 1994; Uc et al., 2006) are also relevant. Individuals with impairments in these domains are more likely than unimpaired drivers to commit errors that cause motor vehicular crashes. Some errors can be detected because drivers normally monitor their performance. When feedback on driving performance fails to match expectations, the discrepancy is often identified “online” (Wickens, 1992) and drivers can take corrective action. Drivers with cognitive deficits are less likely to realize their errors or impaired status.

Sensation and Perception Automobile driving requires selective processing of a large volume of continuous and often competing sensory and perceptual cues from vision, hearing, vestibular, and somatosensory (tactile or haptic and vibratory) sources. Visual cues are especially important to driving (e.g., Hills, 1980) because they convey long-range information about driver self-­trajectory (egomotion), changes in the terrain, and the trajectories of other objects on a potential collision course with the driver. A host of static and dynamic visual cues provide indispensable information on the structure, distance, and time to contact other objects that may arise unexpectedly across the panorama. We survey the world with binocular visual fields that normally span about 180 degrees. The fovea has the highest acuity and spans about 3 degrees around fixation; the macula spans about 10 degrees and participates in detail-­oriented tasks such as map reading and sign localization. The peripheral visual fields have low visual acuity but good temporal resolution and movement detection. Common patterns of visual loss correspond to different diseases and lesions in visual pathways and create a variety of risks for drivers (Rizzo & Kellison, 2004). Crashes and traffic violations in the 3-year records of 17,500 California driver’s license applicants increased with impairments of static visual acuity, dynamic visual acuity (i.e., acuity for moving letter shapes), glare recovery, and visual fields (Burg, 1968). Common eye disorders cause visual sensitivity loss and visual field impairments that can impair driver safety (American Academy of Ophthalmology Policy Statement, 2006). For example, cataracts are a risk factor for car crashes (Owsley, Stalvey, Wells, Sloane, & McGwin, 2001) and are treatable with surgery. Ubiquitous in aging eyes, they cause a reduction in acuity and the creation of distracting reflections (e.g., halos around lights) or glare. Glaucoma can affect driving by producing both visual impairments and visual field loss. Macular degeneration affects areas of high-­detail vision around fixation. Retinitis pigmentosa, an inherited condition that tends to

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affect younger drivers, constricts the peripheral visual fields, causing inability to detect objects approaching from the side. Even glasses may cause trouble while driving due to reflections, distortions, or discontinuities (e.g., caused by looking across bifocal or trifocal lenses or glare). Glare is a disabling effect of intense light and reflections off object surfaces or ocular media that can veil our perception of critical environmental targets (Stiles & Crawford, 1937). For instance, glare from the headlights of oncoming traffic can mask information on the changing terrain and locations of nearby vehicles. Drivers with lesions of visual areas in the occipital lobe and adjacent temporal and parietal lobes have various visual field defects (e.g., homonymous hemianopia or quadrantanopia) and may fail to perceive objects or events in the defective fields (Rizzo & Barton, 2005). Search strategies to compensate for the visual defect may create extra work that distracts from the driving task. Lesions in the occipital and parietal lobes (in the dorsal or “where” pathway) may have greater effects on driving performance than do lesions in the occipital and temporal lobes (in the ventral or “what” pathway). Dorsally located lesions produce visual loss in the ventral or lower visual fields that may obscure the view of the vehicular controls and much of the road ahead of the driver. In addition, dorsal visual pathway lesions can impair processing of movement cues, as in cerebral akinetopsia (cerebral motion blindness), and reduce visuospatial processing and attention abilities. Some patients with these types of lesions have impairments in visual search and the useful field of view (UFOV; i.e., the visual area from which information can be acquired without moving the eyes or head) and are clearly unfit to drive. One example is the hemineglect syndrome, which results in the failure to orient to targets in the left visual hemifields in patients with right parietal lobe lesions (Rizzo & Barton, 2005). Another is Bálint’s syndrome (simultanagnosia/spatial disorientation, optic ataxia, and ocular apraxia), which generally involves bilateral dorsal visual pathway lesions. Ventrally located lesions produce upper-­visual-field defects that may be less troublesome for driving; however, they may also cause impairments in (1) object recognition (visual agnosia) affecting interpretation of roadway targets, (2) reading (pure alexia) affecting roadway sign and map reading, and (3) color perception (cerebral achromatopsia), impeding use of color cues in decoding traffic signals and road signs and detection of roadway boundaries and objects defined by hue contrast (Rizzo & Barton, 2005). Drivers with cerebral lesions may have various deficits that affect perception of structure and depth and are not measured by standard clinical tests. The brain employs multiple cues on object structure and depth because this information is so critical for interacting with moving objects and obstacles (Palmer, 1999). Binocular stereopsis (stereo vision) and motion parallax provide unambiguous cues to relative depth. For motion parallax, moving the head along the interaural axis produces relative movement of objects. The orderly relationship between relative velocities of images across the retina and relative distances of objects in the scene provides cues to structure and depth. Motion parallax impairments may contribute to vehicular crashes when impaired drivers must make quick judgments with inaccurate or missing perceptual information on the location of surrounding obstacles (Nawrot, 2001). Detecting and avoiding potential collisions require information on approaching objects and the driver’s vehicle. Objects set to collide with the driver stay at a

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fixed location in the driver’s field of view, whereas “safe” objects move to the left or right. Time to contact (TTC) is estimated from the expanding retinal image of the approaching object. Older drivers are less accurate than younger drivers at detecting an impending collision during braking (Andersen, Cisneros, Atchley, & Saidpour, 1999; Andersen, Cisneros, Saidpour, & Atchley, 2000) and judging if an approaching object will crash into them (Andersen, Saidpour, & Enriquez, 2001). Performance is worse for longer TTC conditions, possibly due to a greater difficulty in detecting the motion of small objects in the road scene ahead of the driver (Andersen, Saidpour, & Enriquez, 2001). Displacement of images across the retina during travel produces optic flow patterns (Gibson, 1979) that can specify the trajectory of self-­motion (egomotion) with accuracy (Warren & Hannon, 1988; Warren, Mestre, & Morris, 1991). Perception of heading from optical flow patterns is optimal in a limited part of the flow field surrounding the future direction of travel (Mestre, 2001). On curved roads, drivers tend to fixate the information flowing from the inside edge of the road where the curve changes direction (Land & Lee, 1994). The findings are relevant to detection of collisions, design of roads, and positioning of traffic warnings within a driver’s dynamic visual environment, and may be interpreted in terms of a dynamic UFOV (see the section on attention and driving below). Perception of structure from motion (SFM; also known as “kinetic depth perception”) whereby subjects see the three-­dimensional structure of an object defined by motion cues, is a likely real-world use of motion cues that may fail in drivers with cerebral lesions. SFM can be measured using a task in which subjects perceive shapes defined by random dot elements that move among varying amounts of random dot noise (see Figure 7.2); this ability is impaired in akinetopsia and early Alzheimer’s disease (Rizzo & Nawrot, 1998). SFM deficits have been associated with greater risk for safety errors and car crashes in driving simulation scenarios (Rizzo, 2001; Rizzo, Reinach, McGehee, & Dawson, 1997).

FIGURE 7.2.  Screenshot from the perception of structure from motion (SFM) test.

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Executive Functions and Driver Behavior Executive functions provide control over information processing and are a key determinant of driver strategies, tactics, and safety. These functions include decision making, impulse control, judgment, task switching, and planning (e.g., Benton, 1991; Damasio, 1996, 1999; Rolls, 1999, 2000). Executive functions strongly interact with working memory (i.e., the process of briefly storing information so that it is available for use) and attention, which operates on the contents of working memory (Baddeley & Logie, 1992; Cabeza & Nyberg, 2000; Dias, Robbins, & Roberts, 1996; Norman & Shallice, 1986). The mapping between executive functions measured in laboratory settings and real-life driving performance can be addressed using a set of theoretically motivated tasks. Table 7.1 shows hypothesized relationships between off-road cognitive (executive function) tests and specific driving behaviors.

Decision Making Decision making requires the evaluation of immediate and long-term consequences of planned actions. Impaired decision making appears to be a critical factor in driver errors that lead to vehicular crashes (van Zomeren, Brouwer, & Minderhoud, 1987). Causes of impaired decision making include acquired brain lesions affecting prefrontal areas (due to stroke, trauma, or neurodegenerative impairment), antisocial personality disorder, effects of drugs and alcohol (Bechara, Tranel, Damasio, & Damasio, 1996; Fuster, 1996; Rizzo, Sheffield, & Stierman, 2003; Rolls, Hornak, Wade, & McGrath, 1994; Stuss, Gow, & Hetherington, 1992), and fatigue (Jones & Harrison, 2001; Paul, Boyle, Rizzo, & Tippin, 2005). Driving outcomes of impaired decision making could include traffic violations (e.g., speeding), unsafe vehicular maneuvers, engaging in extraneous and unsafe behavior while driving, and, of course, crashes.

Go/No-Go Decision Making Driver strategies include deciding on a sequence of trips or stops (for gas, food, directions, or naps), evaluation of traffic and weather risks, and making go/no-go decisions regarding whether to take a trip. Driving outcomes of go/no-go decisions could include adapting to speed changes near a school, choosing to switch on the headlights at twilight or in rain, changing gears on a hill, and deciding whether and when to overtake another vehicle, change lanes in traffic, or pass through intersections and traffic signals. Abstract virtual environments can be used to assess go/no-go decision-­making behavior in a driving-like task. Using a personal computer equipped with a steering wheel and pedals, subjects (28 with and 22 without cognitive impairments) drove through intersections that had gates that opened and closed (Rizzo, Severson, Cremer, & Price, 2003; Rizzo, Sheffield, et al., 2003). A green “Go” or red “Stop” signal appeared at the bottom of the display as the subject approached the gate, and a gate­closing trigger point was computed. Cognitively impaired drivers who had frontal lobe damage, such as shown in Figure 7.3, had more crashes into closed gates, more failures to go at open gates, and longer times to complete the task. These findings suggest a failure of response selection criteria based on prior experience, as previously

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TABLE 7.1.  Hypothesized Relationships between Tests of Executive Function and Driving Behaviors Test name

Ability measured

Driving behavior

Reference

Iowa Gambling Task

Decision making

Traffic violation (e.g., speeding); engaging in behavior extraneous to driving

Bechara et al. (1994)

Go/No-Go

Decision making and response inhibition

Running red light; timing of left turn across traffic; engaging in behavior extraneous to driving; stopping at or continuing through a yellow light

Podsiadlo & Richardson (1991)

Tower of Hanoi

Planning and execution of multistep tasks

Sudden brake application; swerving across lanes; running car near empty; viewing a map while driving (extraneous behavior)

Lezak (1995)

Wisconsin Card Sorting Test

Response to changing contingencies

Failure to adjust speed or following distance in response to changing road conditions

Lezak (1995)

Trail Making Tests A and B

Response alternation

Failure to alternate eye gaze appropriately between road, mirrors, and gauges

Lezak (1995)

Stroop Color and Word Test

Response inhibition, impulse control

Glances of > 2 seconds off road; e.g., with passenger present or while eating; failing to pull over for emergency vehicle or making inappropriate maneuver for emergency vehicle; speeding up to prevent another driver from merging

Lezak (1995)

AX-Continuous Processing Task

Working memory, response inhibition, impulse control

Running red light; following lead car through intersection; following familiar routes even though intending to deviate

Beck et al. (1956)

Controlled Oral Word Association

Cognitive fluency and flexibility (verbal)

Slowed processing of verbal traffic signs; failure to adjust to altered driving conditions (e.g., slowing in response to construction signs)

Lezak (1995)

Design Fluency

Cognitive fluency and flexibility (nonverbal)

Slowed processing of symbolic or pictorial traffic signs; failure to adjust driving in response to such signage

Lezak (1995)

reported in individuals with decision-­making impairments on a gambling-­related task (e.g., Bechara, Damasio, Tranel, & Damasio, 1997). Drivers who had lesions in areas that did not produce executive dysfunction performed well on the go/no-go task, supporting the specificity of this task in localizing decision-­making impairments in a driving-like task.

Surveillance of Driver Decisions at Traffic Intersections Real-world patterns of driver go/no-go decision making can be evaluated from experimental observations of many drivers as they pass through traffic intersections.

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FIGURE 7.3.  Three-dimensional magnetic resonance imaging reconstruction of the brain surface shows a right frontal lobe lesion (dark areas) in a subject who participated on the go/no-go decision-­making task

Hanowski, Wierwille, Garness, and Dingus (2000) and Wierwille, Hanowski, and Hankey (2002) used video surveillance to assess driver errors at intersections with stop signs or traffic lights during high-­volume traffic, providing an evaluation of realworld driver go/no-go decision making. The analyses resulted in the development of probability taxonomies that provide a framework (decision tree) for analyzing critical incidents in large volumes of data. For every 10,000 drivers entering the intersection, 3.3% made some sort of driver error: 1.5% during left turns, 0.5% during right turns, 0.4% going forward, and 0.9% during other activities; 41/10,000 drivers ran through red lights—31 on left turns, 8 going forward, and 2 on right turns. (For similar results, see Bonneson, Zimmerman, & Brewer, 2002; Fakhry & Salaita, 2002; Retting, Ulmer, & Williams, 1999; Retting, Williams, Farmer, & Feldman, 1999.) For intersections with stop signs, there was a 3.0% overall probability of a drivererror critical incident of any type. Most occurred during left turns (1.5%), followed by going forward (0.7%), right turns (0.2%), and other scenarios (0.6%). The overall rate of running the stop sign was 19/10,000 vehicles, and the rate for a rolling stop was similar, at 15/10,000 vehicles, for a total stop sign violation rate of 34/10,000 vehicles. (For higher stop-sign violation rates, see Fakhry & Salaita, 2002; Pietrucha, Opiela, Knoblauch, & Crigler, 1989.) Hanowski and colleagues (2000) and Wierwille, Hanowski, and Hankey (2002) showed that red-light-­running events are also common critical incidents, occurring at a rate of about 3%. However, these observations of general traffic do not provide specific insight into the causal mechanisms or the precipitating or contributing factors in impaired drivers. Greater insight is needed into specific decision-­making mechanisms in these situations in order to identify possible intervention strategies.

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Impulse Control/Response Inhibition Drivers with impaired decision making may also show impairments in impulse control and response inhibition. Impulse control is related to decision making but does not involve evaluation of immediate and long-term consequences (Barratt, 1994; Evenden, 1999). “Impulsiveness” can be perceptual, cognitive, or motor. Motor impulsiveness may be “nonaffective,” as on the Stroop (1935) test, in which subjects must identify the color of ink used to print a conflicting color name by inhibiting the compulsion to read the color name. Affective motor impulsiveness occurs when a person cannot inhibit a habit of responding to a stimulus that predicts a reward with affective value (Zuckerman, 1996), as when a driver impulsively speeds up to prevent another car from merging ahead. In perceptual impulsiveness, failure of inhibition occurs at the level of working memory, before a response can be readied and executed. Observers may have more trouble identifying a visual target among distracters if the distracters are familiar. For instance, a driver traveling in a stable convoy of vehicles may follow the convoy through an intersection without noticing that the signal has turned red. Cognitive impulsiveness reflects inability to evaluate the outcome of a planned action and may give the appearance of failure to perceive or evaluate risk. For example, a driver may embark on a long road trip despite poor weather conditions or an unsound vehicle. Perceptual impulsiveness resembles “lapses” in the Reason taxonomy (Reason, 1984) of error. Lapses represent failure to carry out an action rather than commission of an incorrect action. Lapses may be caused by the interruption of an ongoing sequence by another task, and they give the appearance of forgetfulness. For example, a driver returning home from work may begin talking on a cell phone and miss (“forget” to take) a highway exit. Disinhibition failures in executive dysfunction may contribute to “slips,” errors in which an intention is incorrectly executed because the intended action sequence departs slightly from routine. Slips may resemble inappropriate but more frequent actions and are relatively automated (Norman, 1981). In this case, behavior is guided by a contextually appropriate strong habit due to lack of close monitoring by attention. A driver whose destination requires deviation from a familiar route may make a wrong turn toward a more habitual destination. A driver approaching a tollbooth may be distracted by an onboard warning light, fail to decelerate, and strike a slower lead car. Drivers with executive dysfunction may commit rule-based errors when they believe they understand a situation and formulate a plan by “if–then” rules, but the “if” conditions are not met, a “bad” rule is applied, or the “then” part of the rule is poorly chosen. For instance, a driver may dismiss an engine temperature warning light, fail to service the vehicle, and suffer a vehicular breakdown in traffic. Decision-­making impairment can occur independently of memory impairment, but memory impairment tends to compromise a driver’s decision-­making ability because the driver cannot learn or recall all the situational contingencies required to make optimal decisions. Knowledge-based errors signify inappropriate decision making and planning due to failure to comprehend. In this case a driver may be overwhelmed by the complexity of a traffic situation and lack information to interpret it correctly.

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In practice it is often difficult to determine unambiguously whether an error leading to a critical incident was due to a driver lapse, slip, rule-based, or knowledgebased error. Accordingly, we use a set of specific operational definitions for detecting critical incidents and empirically derived “decision tree” tools for classifying these unsafe incidents and identifying their likely causes. Such empirically derived tools and models provide taxonomic frameworks for organizing and interpreting data on driver error and for identifying common causes and mitigation strategies from seemingly unrelated instances (Dingus, Hetrick, & Mollenhauer, 1999; Grayson, 1997; Huguenin, 1997; Rizzo, McGehee, Dawson, & Anderson, 2001; Wilde, 1982).

Attention and Working Memory A critical executive function related to driving is the continuous direction of attention to relevant features of the driving environment. We remember and act upon attended stimuli, not unattended items. There is increasing evidence that specific regions of the prefrontal cortex are essential in directing cognitive resources toward the accomplishment of tasks with a wide range of memory demands (Cabeza et al., 2003: Nyberg et al., 2003). Defects of attention clearly impair driver decisions (e.g., Ball, Owsley, Sloane, Roenker, & Bruni, 1993; Owsley, Ball, Sloane, Roenker, & Bruni, 1991; Parasuraman & Nestor, 1991; Rizzo, McGehee, Dawson, & Anderson, 2001; Rizzo, Reinach, McGehee, & Dawson, 1997) and affect a variety of processes (e.g., Parasuraman & Davies, 1984). Automatic attention processes are fast and involuntary and should contribute to subconscious corrections during driving, including control of steering wheel or accelerator pedal position during uneventful driving on mundane highway segments (Dingus, Hardee, & Wierwille, 1987; McGehee, Lee, Rizzo, Dawson, & Bateman, 2004). Controlled attention processes are slow and operate during capacity-­demanding tasks and conscious decision making. Examples include glancing between the road and rearview mirror while maneuvering in and out of a traffic convoy, or the deliberate surveillance of a busy intersection with changing traffic signals, using head and eye movements. This is a “dilemma zone” where critical go/no-go decisions (see above) must be made (Mahelel, Zaidel, & Klein, 1985). The decision to accelerate or brake depends on driving speed and the time for which the green or yellow signal is visible (Allsop, Brown, Groeger, & Robertson, 1991). Owsley and colleagues (1991) and Ball and colleagues (1993) linked driving impairment with reduction in the useful field of view (UFOV), the visual area from which information can be acquired without moving the eyes or head (Ball, Beard, Roender, Miller, & Griggs, 1988). Performance on the UFOV task depends on speed of processing and divided and selective attention. The UFOV begins to deteriorate by age 20. This deterioration may reveal itself as a shrinking of the field of view or a decrease in efficiency with which drivers extract information from a cluttered scene (Sekuler, Bennett, & Mamelak, 2000). Diminished efficiency in advancing age is worse when attention is divided between central and peripheral visual tasks. Driver performance may also change when attention is divided between the road and an onboard task, and gaps of greater than 2 seconds may interrupt scanning of the road (Wierwille, Hulse, Fischer, & Dingus, 1988).

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Focused Attention Executive functions control the focus of attention (Vecera & Luck, 2003). Without focused attention, we may be unaware of marked changes in an object or a scene made during a saccade, flicker, blink, or movie cut; this is known as “change blindness” (O’Regan, & Rensink, 1999; Rensink, O’Regan, & Clark, 1997, 2000; Simons & Levin, 1997). Traces of retinal images in visual working memory fade without being consciously perceived or remembered (“inattentional amnesia”). The very act of perceiving one item in a rapid series of images briefly inhibits ability to perceive another image, the “attentional blink” (Rizzo, Akutsu, & Dawson, 2001). Focused attention is thought to permit consolidation of information temporarily stored in visual working memory. Perceptual errors are likely if working memory is still occupied by one item when another item arrives, due to interference or limitations in capacity, which at a bottleneck stage admits only one item at a time. Failure to detect roadway events increases when information load is high, as at complex traffic intersections with high traffic and visual clutter (Batchelder, Rizzo, Vanderleest, & Vecera, 2003; Caird, Edwards, & Creaser, 2001). Driver errors occur when attention is focused away from a critical roadway event in which vehicles, traffic signals, and signs are seen but not acted upon, or are missed altogether (Treat, 1980). Sometimes eye gaze is captured by irrelevant distracters (Kramer, Cassavaugh, Irwin, Peterson, & Hahn, 2001; Theeuwes, 1991), such as “mudsplashes” on a windscreen that prevent a driver from seeing a critical event (e.g., an incurring vehicle or a child chasing a ball; O’Regan et al., 1999). Drivers with cerebral lesions are liable to be “looking but not seeing,” even under conditions of low information load (Rizzo & Hurtig, 1987; Rizzo et al., 1997; Risso, Akutsu, & Dawson, 2001) and resembling the effects observed in air-­traffic controllers during prolonged, intensive monitoring of radar displays.

Shifting Attention Safe driving requires executive control to shift the focus of attention among critical tasks such as tracking the road terrain; monitoring the changing locations of neighboring vehicles; reading signs, maps, traffic signals, and dashboard displays; and checking the mirrors (Owsley et al., 1991). These tasks require an ability to shift attention between disparate spatial locations, local and global object details, and different visual tasks. Drivers must also shift attention between modalities when they drive while conversing with other occupants, listening to the radio or tapes, using a cell phone, or interacting with in-­vehicle devices (Kantowitz, 2000, 2001). These attentional abilities can fail in drivers with visual processing impairments caused by cerebral lesions (Rizzo et al., 2004; Vecera & Rizzo, 2004). Functional neuroimaging studies show changes in frontal lobe activity with driver modulation of vehicular speed (Calhoun et al., 2002; Peres, van de Moortele, Lehericy, LeBihan, & Guezennez, 2000) and with alcohol-­impaired driving (Calhoun, Pekar, & Pearlson, 2004). These studies also suggest that engaging in conversation distracts the brain from processing information in a visually demanding task such as driving, and vice versa (Just et al., 2001). Cell phone conversation disrupts driving performance by diverting attention to an engaging cognitive context other

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than the one immediately associated with driving (Strayer & Johnston, 2001). A host of other modern “infotainment” devices also distract cognitive resources away from the driving task (Ehret, Gray, & Kirschenbaum, 2000; Kantowitz, 2000). The interference occurs at the level of central processes that can be disrupted by cerebral lesions. Relevant interactions in aging and brain injury can be measured by administering a controlled auditory verbal processing load during driving tasks (Boer, 2001; Kantowitz, 2001; Rizzo et al., 2004).

Metacognition Metacognition is the awareness of one’s own thought processes and efficacy. This self-­awareness of cognitive processes can include specific contexts and strategies to enhance understanding (e.g., “My brain works best before lunchtime”; “I should keep lists”) (Fernandez-Duque, Baird, & Posner, 2000; Garner, 1988). Metacognition has been linked with executive functions by cognitive, developmental, and educational psychologists studying control of cognition, the developing awareness of the mind in children, and effects of awareness on learning and academic success (Mazzoni & Nelson, 1998; Shimamura, 1994). Some neuropsychologists and cognitive scientists study the anatomy of self-­awareness (Fernandez-Duque et al., 2000; Mazzoni & Nelson, 1998) and awareness of impairments in neurological and psychiatric patients (e.g., with Korsakoff’s amnesia, aphasia, schizophrenia, neglect syndrome) (McGlynn, 1998; Shimamura, 1994), in patients with prefrontal lesions (Schacter, 1998; Stuss & Knight, 2002), and in those with alcohol and drug effects or fatigue and sleep deprivation. Metacognition depends on the coordinated activity of multiple brain areas (McGlynn, 1998) and is relevant to driver safety in terms of awareness of (1) cognitive functions, (2) driving behavior; (3) vehicular performance, (4) road conditions, (5) rules of the road, (6) self-­impairment, and (7) compensatory strategies to mitigate the effects of impairment. Lack of awareness of impairments (anosognosia) may exacerbate the consequences of impairments in other aspects of cognition (Anderson & Tranel, 1989). Drivers who lack awareness of their impaired cognition and behavior are liable to place themselves and others in harm’s way while driving because they fail to take steps that might compensate for their impairments. Predictions of driver safety may fail because drivers may behave differently in the real world than might be expected based on tests in the clinical laboratory (Reger et al., 2004). The relationships between disease status, clinical measures of cognition and awareness, and driving performance may help clinicians working with patients and families to improve or rehabilitate cognitively impaired drivers (Eby, Molnar, Shope, Vivoda, & Fordyce, 2003). Natural and naturalistic studies in the field (see below) can help describe more accurately how cognitive dysfunction affects everyday behaviors, including automobile driving (Rizzo, Robinson, & Neale, 2006).

Emotions and Personality Driving a motor vehicle can be a major source of annoyance, especially to aggressive drivers (Sivak, 1983) who may curse, shout, gesticulate, speed, flash their

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lights, ignore traffic signals, fail to signal, tailgate, drive under the influence of drugs and alcohol, or even use their car to block or strike another car or a pedestrian. “Road rage” is a media-­coined term used to describe extremely aggressive and often criminal events (Brewer, 2000). Aggressive drivers are more likely to be young, male, single, use alcohol or drugs, have a premorbid personality disorder, and experience increased levels of stress at home, work, and in a car (Deery & Fildes, 1999; DiFranza, Winters, Goldberg, Cirillo, & Biliouris, 1986; Holzapfel, 1995). Car-­related stresses may include crowded roadways, vehicular breakdowns, getting lost, and slow drivers ahead. Stressful life events, such as disruption of personal relationships, may precede some car crashes. Having a gun in the car is a marker for dangerous and aggressive driving behavior (Miller, Azrael, Hemenway, & Solop, 2002). Personality factors associated with aggressive driver crash involvement are thrill seeking, impulsiveness, hostility/aggression, and emotional instability (Beirness, 1993; Dahlen, Martin, Ragan, & Kuhlman, 2005; Jonah, 1997; Schwebel, Severson, Ball, & Rizzo, 2006). Crash-prone drivers have been described as emotionally immature, irresponsible, antisocial, and poorly adjusted, with a history of a traumatic childhood, delinquency, family disruption, and poor work records (Suchman, 1970). Psychiatric factors related to impaired driving ability include alcoholism, antisocial personality disorder, depression, and psychosis (Noyes, 1985; Tsuang, Boor, & Fleming, 1985). A driver with depression may fail to focus attention adequately on the road. A driver with schizophrenia may be distracted by pathological thoughts or hallucinations. Antipsychotic, antidepressant and anxiolytic medications may slow driver reaction times and decrease driver arousal. The impacts of personality and personality disorders, aggression, risk taking, psychiatric disorders, and drugs on driver safety and crash risk require further study.

Arousal, Alertness, and Fatigue Attention, perception, memory, and executive functions that are crucial to the driving task are critically affected by drugs and fatigue (Dinges, 2000), and many vehicular crashes are caused by sleepy drivers (Horne & Reyner, 1995, 1996; Laube, Seeger, Russi, & Bloch, 1998; Leger, 1994; Lyznicki, Doege, Davis, & Williams, 1998), including busy health care personnel (Barger et al., 2005). Sleep deprivation may cause neurologically normal, high-­performing young adult airline pilots to perform as if they have visual constriction or simultanagnosia, as in Bálint’s syndrome (Russo, Thorne, & Thomas, 1999; Thorne, Thomas, & Russo, 1999). Drivers with sleep disorders such as obstructive sleep apnea syndrome (OSAS), are at particular risk for a crash (George & Smiley, 1999; Horstmann, Hess, Bassetti, Gugger, & Mathis, 2000; Young, Blustein, Finn, & Palta, 1997). They may minimize the degree to which they are sleepy (Dement, Carskadon, & Richardson, 1978; Engleman, Hirst, & Douglas, 1997) and fail to recognize that they are having trouble driving. Some drivers in sleep-­related crashes deny having felt tired beforehand (Jones, Kelly, & Johnson, 1979), and sleep-­deprived truck drivers often underestimate their fatigue (Arnold et al., 1997). Symptom minimization may be intentional or due to

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unawareness of sleepiness (Stutts, Wilkins, & Vaughn, 1999), possibly due to an altered frame of reference for fatigue. Sleep disturbances may accompany the hallmark motor, cognitive, psychiatric, and autonomic disturbances in Parkinson’s disease (PD), due to varied involvement of noradrenergic, cholinergic, and serotoninergic systems. Excessive daytime sleepiness or “sleep attacks” have been reported secondary to the use of dopaminergic medications for treatment of PD (Ferreira, Galitzky, & Brefel-­Courbon, 2000; Ferreira, Galitzky, Montastruc, & Rascol, 2000; Ferreira, Pona, & Costa, 2000; Ferreira, Thalamas, & Galitzky, 2000; Frucht, Greene, & Fahn, 2000; Frucht, Rogers, Greene, Gordon, & Fahn, 1999; Hauser, Gauger, Anderson, & Zesiewicz, 2000; Homan, Wenzy, & Suppan, 2000; Lang & Lozano, 1998a, 1998b; Montastruc, Brefel-­Courbon, & Senard, 2000; Paladini, 2000; Ryan, Slevin, & Wells, 2000; Schapira, 2000). The reported lack of warning before falling asleep might actually point to amnesia or lack of awareness for the prodrome of sleepiness (Olanow, Schapira, & Roth, 2000). The border between wakefulness and sleep is indistinct, and falling asleep can be conceived as a process characterized by decreasing arousal, lengthening response time, and intermittent response failure (Ogilvie, Wilkinson, & Allison, 1989). Gastaut and Broughton (1965) found that 2–4 minutes of electroencephalograph (EEG)defined sleep must elapse before more than half of subjects recognize that they had actually been sleeping. The EEG may show progression from wakefulness to Stages I and II sleep, or be preceded by “microsleeps” in which the EEG shows 5 or more seconds of alpha dropout and an increase in theta activity (Harrison & Horne, 1996) (Figure 7.4a). These periods of approaching sleep onset have been correlated with subjective sleepiness among long-haul truck drivers (Kecklund & Akerstedt, 1993) and healthy, sleep-­deprived drivers (Horne & Reyner, 1996; Reyner & Horne, 1998), and with deteriorating driving simulator performance in healthy, sleep-­deprived drivers (Horne & Reyner, 1996; Reyner & Horne, 1998) and OSAS patients (Paul et al., 2005; Risser, Ware, & Freeman, 2000). Further research is needed to address how cognitive errors in the driving task increase as a function of severity of sleep disturbances, measured by polysonography (PSG) and the Multiple Sleep Latency Test (MSLT). Continuous positive airway pressure (CPAP) therapy in drivers with OSAS should lead to improvement in cognitive function, driving performance, and awareness of impairment, and is an area for further research. Observed drowsiness can be quantified through physiological and cognitive performance measures (see Figure 7.4b). Self-­reported estimates of acute drowsiness can be obtained using the Stanford Sleepiness Scale (Hoddes, Zarcone, Smythe, Phillips, & Dement, 1973) and of chronic drowsiness using the Epworth Sleepiness Scale (Johns, 1991). Physiological indices of impending sleep can be measured aboard a vehicle, using a variety of techniques. These include EEG (e.g., of drowsiness or microsleeps), decreased galvanic skin response (GSR), increased respiratory rate, increased heart rate variability, reduced electromyograph (EMG) activity (e.g., cervical paraspinous muscles), and percent eyelid closure (PERCLOS). PERCLOS scores of 80% or greater are highly correlated with falling asleep (Dinges, 2000). The lid closure can be used to trigger auditory or haptic warning (e.g., vibrating seats in a long-haul truck), which may prevent sleep- or drowsiness-­related crashes.

(a)

5 sec Microsleep

(b)

FIGURE 7.4.  (a) Segment of EEG depciting a microsleep. (b) EEG recording during simulated driving. EEG surface electrodes are connected to the head of a behaving driver seated within the simulator cab and responding to challenges in an experimental driving scenario. 182

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Drug Effects Certain medications have been associated with greater relative risk of automobile crashes in the epidemiological record (e.g., Ray, Thapa, & Shorr, 1993). Antidepressants, pain medications, antihistamines, anticonvulsants, antihypertensives, antilipemics, hypoglycemic agents, sedatives, and hypnotics have all been implicated. Aside from general drowsiness, the specific mechanisms whereby these medications impair driving performance, remain unclear. Drug use, including use of prescription medications, may cause as many fatal accidents as alcohol consumption (Centers for Disease Control and Prevention, 2006). Alcohol and illicit drugs such as marijuana (e.g., Lamers & Ramaekers, 2001) and methylenedioxy-N-methylamphetamine (MDMA) (e.g., Logan & Couper, 2001) also pose serious driving safety risks. One study examining the effects of alcohol alone, marijuana alone, and their combined effects reported significant impairment in driving ability following administration of alcohol or marijuana alone, whereas combining the two substances resulted in “dramatic” impairments in such driving-­related phenomena as time driven out of lane and standard deviation of lateral position (Ramaekers, Robbe, & O’Hanlon, 2000). Driving performance is often impaired at legally defined cutoffs for sobriety (usually 0.8–1.0 mg/dl of ethanol in the United States and 0.5 mg/dl in Europe). Deleterious effects of drugs on driving seem likely to depend partly on neurotransmitter systems involved in “executive functions” that are known to be critical for driving: decision making and working memory. According to a “somatic marker hypothesis” (Damasio, 1994, 1996), decision making is largely guided by somatic (emotional) signals linked to prior experiences with reward and punishment. The generation of somatic states is linked to a neural system that includes the ventromedial (VM) prefrontal cortex, amygdala, and somatosensory cortices (SI, SII, and insula). Working memory defects result from dysfunction in a neural system in which the dorsolateral prefrontal cortex is a critical region (see above). Elucidation of the chemical substrates (e.g., serotonin, dopamine, acetylcholine) that modulate frontal lobe functions in at-risk older drivers may help guide development of pharmacological interventions that improve cognitive performance in the driving task. Studies of the effects of pharmacological agents on driving performance can be conducted most safely in a driving simulator (Lamers, Bechara, Rizzo, & Ramaekers, 2006).

Assessment of Driving Performance Road Tests States generally consider a road test, conducted under the direct observation of a trained expert, to be the “gold standard” of driver fitness. The expert grades driver performance on several driving tasks to calculate a cutoff score to classify a driver as safe or unsafe for licensure. However: (1) Road tests were developed to ensure that novice drivers know and can apply the rules of the road, not to test experienced drivers who may be impaired; (2) road testing carries the risk inherent in the realworld road environment; (3) road test conditions can vary depending on the weather, daylight, traffic, and driving course; (4) driving experts may have different biases and

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grading criteria; and (5) there are few data to show that road tests are correlated with crash involvement. There have been several attempts to develop empirically based reliable and valid road tests (e.g., Hunt et al., 1997).

State Records The main data that transportation researchers have on actual collisions and contributing factors are collected post hoc (in forensic or epidemiological research). These data are highly dependent upon eyewitness testimony, driver memory, and police reports, all of which have serious limitations. The best information regarding near collisions generally comes from anecdotal reports by driving evaluators and instructors (usually testing novice drivers) and police reports of moving violations. Most of these potential crash precursors, if they are even recognized, are known only to the involved parties and are never available for further study and subsequent dissemination of safety lessons.

Driving Simulators Driving simulators have been applied to (1) quantify driver performance in cognitively impaired drivers, (2) study basic aspects of cognition in drivers with brain lesions, (3) probe the effects of information-­processing overload on driver safety, and (4) optimize the ergonomics of vehicular design. Driving simulation offers advantages over the use of road tests or driving records in assessments of driver fitness. Simulator studies provide the only means to replicate exactly the experimental road conditions under which driving comparisons are made, and simulations are safe, with none of the risk of the road or test track. Simulation has been successfully applied to assess performance profiles in drivers who are at risk for a crash due to a variety of different conditions, including sleep apnea, drowsiness, alcohol and other drug effects, old age, Alzheimer’s disease, Parkinson’s disease, HIV, or traumatic brain injury (Brouwer, Ponds, Van Wolffelaar, & Van Zomeren, 1989; Dingus et al., 1987; Guerrier, Manivannan, Pacheco, & Wilkie, 1995; Haraldsson, Carenfelt, Laurell, & Tornros, 1990; Madeley, Hulley, Wildgust, & Mindham, 1990; Marcotte et al., 2006; McMillen & Wells-­Parker, 1987; Rizzo et al., 1997). There are several different types of simulators (e.g., film, noninteractive, interactive, fixed vs. motion based, desktop, full cab; cf. Milgram, 1994). Special concerns are often raised about fitness to drive with Alzheimer’s disease (AD), the most common cause of abnormal cognitive decline in older adults (Cummings & Cole, 2002). Johansson and Lundberg (1997) raised the important concern that the first manifestation of AD may sometimes be a fatal crash and that preclinical AD raises the risk of a crash several fold. Brain autopsies showed neuropathological evidence of possible or probable AD in over half of 98 older drivers who perished in vehicular crashes, yet none had a diagnosis of AD, and family members were often unaware of a problem (Lundberg, Hakamies-­Blomqvist, Almkvist, & Johansson, 1998). Figure 7.5a shows how driving simulation can be applied to study drivers with mild-to-­moderate cognitive impairment due to AD. In this simulation, subjects drive on a virtual highway passing an emergency vehicle (a police car) stopped by the shoulder of the highway. To minimize the chance of contact with the vehicle or nearby

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(a)

(b)

FIGURE 7.5.  (a) Driving simulation requiring avoidance of a stopped police car. (b) Avoidance maneuver of an unimpaired simulator driver.

pedestrian, the driver must perceive, attend to, and interpret the roadway situation, formulate an evasive plan, and then exert appropriate action upon the accelerator, brake, or steering controls, all under pressure of time. Figure 7.5b shows the typical response of a normal individual, that is, slowing and steering around the parked police vehicle. A relative drawback to simulation research is simulator adaptation syndrome (SAS), characterized by autonomic symptoms including nausea and sweating (Stanney, 2002). The discomfort is thought to be due to a mismatch between visual cues of movement, which are plentiful, and inertial cues, which are lacking or imperfect, even in simulators with a motion base (Rizzo, Sheffield, et al., 2003). In our experience SAS is more likely with crowded displays (as in simulated urban traffic), advanced

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age, female gender, and history of migraine or motion sickness. SAS may also result from conflicts between visual cues that are represented with differing success in modern computer displays. Choice of equipment and scenario design (e.g., avoiding sharp curves, left turns, frequent stops, and crowded scenes) can minimize SAS. Another issue in simulator-based research is the need to test the validity of the simulation (e.g., Marcotte et al., 2004). This may involve detailed comparisons of driver performance in a simulator with performance in an instrumented vehicle and with state records of crashes and moving violations in each population of drivers being studied (Rizzo, 2004). The apparent face validity of the simulation—that is, that the driver appears to be driving a car and is immersed in the task—does not guarantee a lifelike performance. Drivers may behave differently in a simulator where no injury can occur, compared to real-life driving situations in which life, limb, and licensure are at stake. They may even perform differently when the same scenario is implemented on different simulator platforms, motivating calls for development of standard scenarios by an international Simulator Users Group (www.engineering. uiowa.edu/simusers). Efforts to improve driving simulators have often focused on making the simulations more “lifelike,” yet the added cost (e.g., of a mechanical motion base) might not translate to better assessments of driver safety. Abstract versions of reality that enhance some critical environmental cues (e.g., dynamic texture or shading) and minimize others might provide more effective tests (of “functional reality”) that correlate even better with actual driver performance (see the description of the go/no-go scenario above). Advances in understanding the role and representations of key visual cues from the environment in dynamic graphical displays should improve the acceptance and measurement characteristics of driving simulator tools. The future application of driving simulation to study drivers with medical impairments will benefit from a standardized approach to scenario design, certification standards for ecological validity of simulator graphics and vehicular dynamics, uniform definitions of measures of system performance, and cost-­effective methods for geo-­specific visual database development (Rizzo, Severson, et al., 2003).

Instrumented Vehicles Instrumented vehicles (IVs) permit quantitative assessments of driver performance in the field, in a real car, under actual road conditions. These natural or naturalistic measurements are not subject to the type of human bias that affects interrater reliability on a standard road test. For these reasons, we developed the multipurpose field research vehicles known as ARGOS (the Automobile for Research in erGOnomics and Safety) and NIRVANA (Nissan–Iowa Instrumented Research Vehicle of Advanced Neuroergonomic Assessment; see Figure 7.6). These vehicles are designed to examine objective indices of driving performance in normal and potentially unfit drivers and to assess the safety and usability of prototype automotive technologies. Each consists of a mid-sized vehicle with extensive instrumentation and sensors hidden within its infrastructure. Internal networks of modern vehicles allow for the continuous communication of detailed information from the driver’s own car (Rizzo, Jermeland, & Severson, 2002). Modern vehicles report variables relevant to speed, emissions controls, and



187

(b)

(a)

NIRVANA

FIGURE 7.6.  (a) Exterior view, (b) interior view, and (c) instrumentation of NIRVANA.

(c)

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vehicular performance, and some vehicles allow more detailed reporting options (e.g., on seatbelt and headlight use, climate and traction control, wheel speed, and antilock brake system activation). Lane-­tracking video can be processed with computer algorithms to assess lane-­keeping behavior. Radar systems in the vehicle can gather information on the proximity, following distance, and lane-­merging behavior of the driver and other neighboring vehicles on the road. Global positioning systems (GPSs) can show where and when a driver drives, takes risks, and commits errors. IVs equipped to detect infrared signals associated with possible cell phone use (without recording conversations) can assess potential driver distraction and risk acceptance. Wireless systems can check the instrumentation and send performance data to remote locations. These developments can provide direct, real-time information on driver strategy, vehicular usage, upkeep, drive lengths, route choices, and decisions to drive during inclement weather and high traffic. The driving assessment in an IV can incorporate segments of “baseline” driving to assess vehicular control on uneventful segments of highway under conditions of low cognitive loading. The drives can also incorporate essential maneuvers such as left turns, right turns, stopping at a stop sign, and maintaining vehicular control. Kinematic measures of driver control during vehicular maneuvers include speed, lateral and longitudinal acceleration, yaw, and others (Gillespie, 1992; Milliken & Milliken, 2003). For example, large lateral accelerations indicate when a driver has swerved to miss an obstacle, whereas large longitudinal accelerations occur when a driver either braked hard or accelerated hard to avoid an obstacle. High yaw rate can indicate if a driver has swerved or is rapidly turning the steering wheel. The kinematics of driving has been documented extensively in the automotive industry and in race cars (Gillespie, 1992; Milliken & Milliken, 2003). In addition, standardized challenges can be introduced that stress critical cognitive abilities during the driving task. These tasks are comparable to scenarios implemented in driving simulators and include route-­fi nding tasks (Uc, Rizzo, Anderson, Shi, & Dawson, 2004, 2005), sign identification (Smothers, Rizzo, & Shi, 2003), and multitasking (i.e., driving while performing distracter tasks, as in holding a conversation or using in-­vehicle devices such as cell phones and navigation equipment; Rizzo et al., 2004). The advantage of using IVs to study patients with relatively specific cognitive impairments is exemplified in the recent findings of preserved procedural knowledge for driving skills in drivers with relatively circumscribed and dense amnesia following bilateral hippocampal and parahippocampal lesions caused by herpes simplex encephalitis (Anderson et al., in press). Radar-­equipped IVs have also provided insights on traffic entry judgments in older drivers with attention impairments (Pietras, Shi, Lee, & Rizzo, 2006). Drivers pressed a button to indicate the last possible moment they could safely cross a road in front of an oncoming vehicle. The speed and distance of the oncoming vehicles were measured and time to contact was calculated. Each driver’s time to cross the roadway was independently measured. Compared to unimpaired drivers, attention-­impaired drivers accepted shorter TTC values, took longer to cross the roadway, and showed shorter safety cushions (the difference of time to contact and time to cross the roadway). A Monte Carlo simulation analysis was used to model how potential differences between the attention-­impaired and nonimpaired groups might influence traffic dynamics and the potential for crashes. It showed that

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these performance differences increased the crash risk of the impaired group by up to 17.9 times that of the nonimpaired group. IVs can also be used to assess excessive risk taking in younger drivers (Boyce & Geller, 2002). Olsen, Lee, and Wierwille (2002) combined IV video and radar data to study lane change decisions in neurologically normal adult drivers. Of 8,667 lane changes, 304 (3.5%) were unsafe because the driver initiated the lane change while a vehicle was nearby in the adjacent lane (e.g., in the blind spot) or was forced to make an evasive maneuver to avoid a crash. Continuous monitoring of radar and video information from the IVs of drivers with a range of cognitive abilities could provide additional insight into mechanisms of error that lead to such critical incidents that car crashes may result (“naturalistic driving”).

Naturalistic Driving Multiple studies have used IVs in traffic safety research (e.g., Dingus et al., 1995; Dingus, Neale, & Garness, 2002; Hanowski et al., 2000; Rizzo et al., 2004). Because an experimenter is present in most cases, drivers are liable to drive in an overly cautious and unnatural manner. Because total data collection times are often less than an hour and crashes and serious safety errors are relatively uncommon, until recently no study has captured precrash or crash data for a police-­reported crash or on general vehicular usage. A person driving his or her own IV is exposed to the usual risk of the real-world road environment without the psychological pressure that may be present when a driving evaluator is in the car. Road test conditions can vary depending on the weather, daylight, traffic, and driving course. However, this is an advantage in naturalistic testing, because repeated observations in varying real-life settings can provide rich information regarding driver risk acceptance, safety countermeasures and adaptive behaviors, and unique insights on the ranging relationships between low-­frequency– high-­severity driving errors and high-­frequency–low-­severity driver errors. Such “brain-in-the-wild” relationships (Rizzo et al., 2006) were explored in detail in a study of naturalistic driving performance and safety errors in 100 neurologically normal individuals, driving 100 total driver years (Dingus et al., 2005; Neale, Dingus, Klauer, Sudweeks, & Goodman, 2005). All enrolled drivers allowed installation of an instrumentation package into their vehicle (78 cars) or drove a new model-year IV provided for their use. Data collection provided almost 43,000 hours of actual driving data, over 2,000,000 vehicular miles. There were 69 crashes, 761 near crashes, and 7,479 other relevant incidents (including 5,568 driver errors) for which data could be completely reduced (see Figure 7.7). Crash severity varied, with 75% being mild impacts, such as when tires strike curbs or other obstacles. Using taxonomy tools to classify all relevant incidents, the majority could be described as “lead vehicle” incidents, however several other conflict types (adjacent vehicle, following vehicle, single vehicle, object/obstacle) occurred at least 100 times each. Driver inattention was deemed to be a factor in most of these incidents. In summary, IVs can gather continuous data over long periods of time in naturalistic studies of driver behavior. These studies, which hitherto relied on questionnaires completed by individuals who may have unreliable memory and cognition, can offer unique insights on vehicular usage by at-risk drivers.

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FIGURE 7.7.  A drowsy driver loses control and swerves into the lane of oncoming traffic. There is a corresponding change in lane tracking position and a dip in speed, as shown by the electronic data from the IV. Courtesy of Dr. V. Neale.

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Countermeasures Cognitive impairment is an important risk factor for vehicular crashes in older adults. Adverse outcomes include side impact collisions at traffic intersections, inaccurate time-to-­contact estimates leading to unsafe traffic entry decisions and rear-end collisions with lead vehicles, and run-off-the-road crashes on curved roads. Cognitive interventions with speed of processing and attention training may help mitigate crash risk in some drivers (see the work of Ball and colleagues, e.g., Ball et al., 1988). Another promising intervention strategy is to develop on-board driver assist and collision warning devices to mitigate the risk of crashes in drivers with cognitive impairments. Ongoing research in our laboratory aims to determine an optimal set of signals for alerting drivers to unsafe behavior and impending traffic conflicts using a driving simulator and then an IV, and to estimate the benefits of the proposed safety interventions across the United States in terms of crashes averted. A key aspect of this research is to develop collision warning algorithms and display parameters. Effective warning systems must promote a timely and appropriate driver response and minimize annoyance from nuisance warnings (Bliss & Acton, 2003; Kiefer et al., 1999). The system’s success depends on how well the algorithm and driver interface match driver capabilities and preferences (Brown, Lee, & McGehee, 2001; Burgett, Carter, Miller, Najm, & Smith, 1998; Lee, McGehee, Brown, & Reyes, 2002; Parasuraman, Hancock, & Olofinboba, 1997; Tijerina, Jackson, Pomerleau, Romano, & Peterson, 1995). Algorithms are calculated to signal when to issue warnings and have strong effects on the safety benefit of collision warning systems. Driver interface is also important because it influences how quickly the driver responds and whether the driver will accept the system. A loud auditory warning might generate a quick response, but frequent loud warnings could undermine driver acceptance by distracting and annoying drivers (Sorkin, 1988). Another key interface characteristic that could affect driver performance and acceptance is the warning modality. Several studies have found that haptic displays (e.g., a vibrating seat, pedals, or steering wheel) improve driver reactions to collision situations (Janssen & Nilsson, 1993; Raby, McGehee, Lee, & Norse, 2000; Tijerina et al., 2000). Driver alerting systems must communicate urgency and minimize annoyance. To express the immediacy of attention required by the situation and minimize confusion, the alert urgency should map systematically to the degree of hazard (Edworthy & Adams, 1996; Haas & Casali, 1995). Different sounds communicate urgency levels (Hellier & Edworthy, 1999). Perceived urgency of sounds changes predictably with fundamental frequency, amplitude envelope, harmonics, interpulse speed, rhythm, repetition, speed change, pitch range, pitch contour, and musical structure (Edworthy, Loxley, & Dennis, 1991); of these, interpulse speed was found to have the strongest influence. People perceive atonal bursts as more urgent. Increasing the number of burst repetitions increased alert urgency, but also irritation. Annoying alerts may attract attention but lead a driver to ignore or disable them. Annoyance, like urgency, can be assessed psychophysically and physiologically (Loeb, 1986). Annoyance may signify a reaction to a sound based on its physical nature, emotional content, novelty, or the situation being judged (Fucci, Petrosino, McColl, Wyatt, & Wilcox, 1997; Kryter, 1985). Annoyance depends on sound loud-

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ness, noisiness, sharpness, roughness, harmony, and tonality (Berglund & Preis, 1997; Khan, Johansson, & Sundback, 1997). Noisiness increases with sound level, duration, frequency, spectrum, complexity, and abruptness of increase (Kryter, 1985). Increasing loudness slowly and decreasing it rapidly is more annoying than increasing it rapidly and decreasing slowly (Nixon, Von Gierke, & Rosinger, 1969). Existing studies have focused on visual or auditory cues in operators with normal or corrected-to-­normal vision and cognition without considering how response patterns might change for impaired observers. Complementing visual cues with cues in another sensory mode speeds reaction time (Nickerson, 1973; Todd, 1912). Haptic cues may speed response and reduce annoyance and offer a promising cue for alerting drivers to critical events in information-rich domains. Haptic warnings have proved more effective than visual cues in alerting pilots to mode changes in cockpit automation (83 vs. 100%), but the warnings did impede concurrent visual tasks (Sklar & Sarter, 1999). One type of haptic cue (torque-based kinesthetic) reduced reaction times more than auditory cues (Gielen, Schnidt, & Van den Heuvel, 1983), and another type (vibrotactile) enhanced reaction time to visual cues (Diederich, 1995). Older drivers may rely more on alert signals because of reduced self-­confidence (Lee & See, 2004) and may use sound and vibration alerts more effectively than visual alerts if they have visual-­processing impairments.

Practical Assessment and Public Policy Demographic and health factors may impact driving ability. Relevant factors are age, education, gender, general health, vision status, mobility, and driving frequency. Frequency of driving can be assessed using a Driving Habits Questionnaire (Ball et al., 1998; Stalvey, Owsley, Sloan, & Ball, 1999). Health status information can be obtained using a checklist of medical conditions (e.g., heart disease, cancer) and when they occurred. Certain medical factors (e.g., use of some medications, having a history of falls, back pain, kidney disease, heart disease, diabetes, stroke, bursitis, visual impairment, sleep apnea) are associated with increased risk of driving errors (see Hu, Trumble, Foley, Eberhard, & Wallace, 1998). Psychological state of the drivers can be collected using the General Health Questionnaire (GHQ; Goldberg, 1972). Medication use can be assessed by asking drivers to bring all prescription and over-the-­counter medication to the clinic. A driver’s chronic sleep disturbance can be assessed from the driver’s self-­report on the Epworth Sleepiness Scale. A relevant visual assessment can include tests of letter acuity (e.g., the ETDRS chart; Ferris, Kassoff, Bresnick, & Bailey, 1982), contrast sensitivity (Pelli, Robson, & Wilkins, 1988), and visual field sensitivity (which is often assessed using automated perimetry) (Trick, 2003). UFOV reduction in patients who have normal visual fields can be demonstrated using visual tasks under differing attention loads (Ball et al., 1993). Overall visual health can be assessed with the National Eye Institute Visual Function Questionnaire–25 (Mangione et al., 2001). Several standardized tasks can be used to assess cognitive abilities that are essential to the driving task (see below). Impaired performance on some of these tasks (e.g., CFT, Trail Making Test Part B) may be especially predictive of driving safety

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risk. Of note, neuropsychological test scores are often corrected (e.g., scaled for age and education) to improve the ability to detect deviations from normative reference groups. However, what matters on the road is pure ability, regardless of demographic characteristics. For example, if a driver exhibits slowed processing speed, it is relevant that he or she is slow compared to all other drivers who might be on the road, not just compared to other drivers in the same demographic group. Consequently, studies that aim to correlate neuropsychological performance with driving performance and to generate predictions of safety in individual drivers should use raw (i.e., not corrected for age, education, or gender) neuropsychological test scores. Alternatively, uncorrected scaled scores could allow for placement of the measures on a common metric and assist in normalizing the distribution. Importantly, though there are some large normative groups that make this approach potentially attractive, evidence suggests that the use of norms based on different normative groups may result in significantly different standard scores (Anderson et al., 2007). It should be noted that no single test is sufficiently reliable to base judgments on fitness to drive, and a variety of sources and approaches are needed (Reger, 2004). Briefly, Judgment of Line Orientation (JLO) assesses visuospatial perception. Visuoconstructional ability is tested using the Rey–­Osterreith Complex Figure Test copy version (CFT-copy) and the block design subtest (Blocks) from the Wechsler Adult Intelligence Scale—­Revised (WAIS-R). The CFT-recall version and the Benton Visual Retention Test (BVRT) test nonverbal memory, while the Rey Auditory Verbal Learning Test (AVLT) indexes anterograde verbal memory. The Trail Making Test Part B (TMT-B) and Controlled Oral Word Association (COWA) test aspects of executive function. These tasks are described in detail elsewhere (e.g., Lezak, 1995). Several approaches have been developed to derive an overall estimate of neuropsychological functioning; for example, the global deficit score, which emphasizes number and severity of deficits by assigning more weight to below-­average performances (Marcotte et al., 2004). We have also found it useful to calculate a composite measure of cognitive impairment (Adstat; Cogstat) by assigning standard T-scores (mean = 50, SD = 10) to each of the tests from the neuropsychological assessment battery (Rizzo, Anderson, Dawson, & Nawrot, 2000; Uc et al., 2005). Mobility can be assessed using versions of the functional reach task and the get-up-and-go task (e.g., Podsiadlo & Richardson, 1991). There are, of course, many other potentially useful tests of vision, cognition, and mobility, as well as of personality and driving habits to consider, depending on the questions being asked and resources, expertise, and time available for testing. The Swedish Road Administration (Vägverket) proposed operational guidelines for assessing fitness to drive in motorists with dementia, based on screening measures such as the Clinical Dementia Rating (CDR) and the Mini-­Mental State Examination (MMSE). Generally, patients with moderate to severe dementia (e.g., cutoffs: MMSE ≤ 17; CDR ≥ 2) should not drive. The American Academy of Neurology (AAN) supports the use of regular testing and optional reporting of cases involving dementia (Dubinsky, Stein, & Lyons, 2000). The American Medical Association/National Highway Traffic Safety Administration (AMA/NHTSA; 2003), American Academy of Ophthalmology (AAO; 2006), American Association of Motor Vehicle Administrators (AAMVA), and the Federal Motor Carrier Safety Administration (FMCSA) have formulated and are revising their own guidelines for at-risk drivers with visual,

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cognitive, or medical impairments, based on the best available current peer-­reviewed evidence. In these policy-­making efforts it is critical to consider the relationship between different driver outcomes (such as having a crash) and driver safety (Table 7.2). The AAN set out to create fair, comprehensive, and accurate guidelines for advising drivers with neurological disease or cognitive impairments as to whether they should continue to drive (Dubinsky et al., 2000). These guidelines searched for well­designed, controlled studies of driving in individuals with AD, using the National Library of Medicine’s MEDLINE database and an evidence-based review of the medical literature from 1966 to 1998 with the search terms “aged” or “Alzheimer’s” and “automobile.” The review did not use the term “driving.” The results suggested that drivers with mild dementia (CDR 0.5) had an increased risk of a crash that was not as great as that tolerated in teenage drivers and less than that of drivers operating a vehicle at common lower legal limits of intoxication (0.8 mg/dl). They recommended that these mildly demented drivers be retested at 6-month intervals. Foley, Masaki, Ross, and White (2000) studied driving cessation in older men with incident dementia in the Honolulu Asia Driving Study (HAAS), a population-based longitudinal study of AD and other dementias of over 3-year durations. Only 22% of the participants in incident cases of diagnosed AD or other dementia with a CDR of 1 were still driving at the time of their evaluation, versus 46% of those with a CDR of 0.5 (30% overall). Reduced vision, grip strength, standing balance, and gait speed contributed to driving cessation. (These were not used in AAN guidelines). Foley et al. estimated that, nationwide, about 4% of men ≥  75 years who drive (~175,000 persons) have very mild or mild dementia (i.e., CDR < 2). Rarely do men with moderate or more severe stage dementia (CDR ≥  2) continue to drive. About 1 in 10 of the demented drivers reportedly used a “copilot” to facilitate driving. The wisdom of using a copilot is unclear, as it may increase the passenger’s risk for a motor vehicular injury or fatality. Foley, Masaki, White, Ross, and Eberhard (2001) took issue with the AAN. Whereas the AAN recommended that patients with AD and CDR of 1 should not TABLE 7.2.  Driving Endpoints and Their Prediction of Unsafe Driving Endpoint

Comment

Reported at-fault accident above baseline rate

Valid, insensitive

Reported at-fault accident without comparison to baseline rate

Less valid, insensitive

License revocation by statute

Valid de factor, insensitive

Driving privileges revoked by family member

Probably valid, insensitive

Self-surrender of license

Probably valid, very insensitive

Failed on-road driving test by blinded professional examiner using statutory criteria

Valid de factor, sensitive, probably the gold standard

Failed on-road driving test by blinded professional examiner using validated research criteria

Valid, sensitive

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drive and that their families should be informed of this clinical recommendation, Foley and colleagues (2001) suggested that patients may not accept the physician’s advice and family members may have trouble complying with this recommendation. The AAN recommended referring patients for a driving evaluation to determine which patients with AD and a CDR of 0.5 are “appropriate” to continue driving, but Foley and colleagues countered that this assumes that this service is readily available and ideally covered by a patient’s health insurance policy; in reality, there are few qualified examiners in the United States, and the charge for an evaluation may not be covered by a typical Medicare-­linked supplemental insurance. There are ~ 14 million drivers ≥  70 years, and the number is growing, so demand for driver evaluation services and insurance coverage will increase. Dubinsky and colleagues (2000) acknowledged that it is difficult to enforce cessation of driving, and adequate testing facilities are lacking. They asserted that the AAN guidelines were developed for health care professionals to point out potential problems associated with allowing even mildly cognitively impaired individuals to drive but were not designed to recommend legislation. State and federal governments are responsible for legislation and enforcement of driving restrictions. Duchek and colleagues (2003) showed that many drivers with CDR scores of 1 or greater are unsafe to drive within a year of their road tests. However, mandatory reporting of drivers with health complaints is controversial because it may inhibit drivers with treatable conditions from seeking required medical attention. The American Medical Association (AMA) recommended that physicians report their patients’ medical conditions when the condition poses a threat and the patient is apparently disregarding the physician’s advice not to drive—with liability protections for goodfaith reporting. The AAN supports the development and promotion of better evaluation tools to assess driver safety, to help physicians recognize when a driver should be referred for evaluation, and to help state officials conduct such an evaluation. This is important because state agencies are not equipped to perform complex assessments of performance in drivers with flagged medical conditions for determination of driving safety. Relevant training and diagnostic tools can be developed in collaborations between state transportation officials and other medical expert groups, including physician and patient organizations. Stricter driving and reporting standards may be needed for drivers who provide public transportation or transport hazardous material. State and federal efforts are needed to plan for and provide transportation resources for individuals who are unable to transport themselves. Health care personnel should review the driving laws in their area and be prepared to discuss and document their medical recommendations in light of these regulations (American Academy of Neurology, 2006).

Conclusions Safe driving requires the coordination of attention, perception, memory, motor and executive functions (including decision making) and self-­awareness or metacognition. These abilities may be impaired by fatigue; overwork; illicit drugs and alcohol; advancing age; medical, neurological, personality, or psychiatric disorders; and pre-

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scription drug effects. Because age or medical diagnosis alone is often an unreliable criterion for licensure, decisions on fitness to drive should be based on empirical observations of performance, preferably under conditions of optimal stimulus and response control in environments that are challenging yet safe. Real-life crashes are sporadic, uncontrolled events during which few objective observations can be made. Personal accounts and even state crash records may be incomplete, and crashes are underreported. In most cases, state road tests are designed to test if novice drivers know and can apply the rules of the road, not to predict crash involvement in veteran drivers who may now be impaired. Linkages between cognitive abilities measured by neuropsychological tasks and driving behavior assessed using driving simulators and natural and naturalistic observations in IVs can help standardize the assessment of fitness to drive. By understanding the patterns of driver safety errors that cause crashes, it may be possible to design interventions to reduce these errors and injuries and increase mobility. These interventions include driver performance monitoring devices, collision alerting and warning systems, road design, and graded licensure strategies.

Acknowledgments This research was supported by Grant Nos. AG 15071, AG 17707, and R01 AG026027 from the National Institute on Aging (NIA).

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Chapter 8

Considerations in the Cross-­Cultural Assessment of Functional Abilities Mariana Cherner

T

he need to assess disability in persons of diverse cultural backgrounds continues to increase, both as a result of immigration patterns around the world as well as the impetus to transfer available technologies from developed countries to more resource­limited settings. Occupational scientists have devoted significant effort to developing awareness about the delivery of culturally competent rehabilitation services, making care providers cognizant of potential mismatches between the professional and the patient with regard to health-­related views, generalizability of activities of daily living, nonverbal communication, and cultural norms (see, e.g., Jezewski & Sotnik, 2001, for a listing of issues and resources pertinent to rehabilitation settings). Similarly, work in cultural psychology and psychiatry, as well as medical anthropology, highlights cultural and sociodemographic differences in the understanding of health, disease, and disability (James & Foster, 1999; Reynolds Whyte & Ingstad, 1995; Truscott, 2000; van der Geest & Reis, 2002). In neuropsychology we have been concerned with the applicability of cognitive assessment methods that were developed and validated primarily in the Western world, and most often in English, to other populations. Increasingly, neuropsychologists have also been interested in the correspondence between performance on cognitive tests and “real-world” functioning, as the latter is not only of practical interest but also a requisite for diagnosing most types of dementing disorders. Efforts to adapt functional assessment instruments for use across different populations have led investigators to address certain basic dimensions that determine equivalence between the original instrument and the adapted one. These dimensions pertain primarily to aspects of construct validity. Once construct validity can be reasonably demonstrated, then the resulting instrument is ready for pilot testing, which may lead to further adjustments. Next, the psychometric properties of the instrument should be examined, leading to other potential adjustments, and finally the instrument can be subjected to norming with representative samples of interest.

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The Adaptation Process Effective adaptations are accomplished by successive approximation. “Adaptation,” in this context, is the overall process of making an instrument appropriate for use in a setting that is different from its original. This may involve translation into another language, translation into regional variants of the same language, and/or replacing certain concepts in an instrument to harmonize with a different cultural, regional, or linguistic context. The section that follows uses translation into a new language to detail the iterative process required to achieve a sound instrument, but the steps involved apply to other mentioned aspects of adaptation and are reflected in the subsequent sections on construct validity.

Translation When translation is required, the method of forward translation into the new language or variant followed by back translation into the original language (typically English) had been advocated by many as necessary to achieve an accurate translation (Brislin, 1970). However, arguably more critical steps are required in order to obtain a usable instrument. First, the translation needs to be performed and subsequently examined by truly bilingual individuals with relevant expertise (e.g., neuropsychologists, medical professionals, occupational therapists) who can determine linguistic and conceptual equivalence and make adjustments to the original translation, as needed. Bonomi and colleagues (Bonomi et al., 1996) exemplify the use of these strategies in their translation of the English version of the Functional Assessment of Cancer Therapy (FACT) into six European languages. In this case, two professional translators produced the first translation to the target language. Then a third independent translator was used to reconcile the two versions, and a fourth translator performed the back translation into English. Next, a panel of three to four bilingual health professionals evaluated the translations and resolved any discrepancies. Finally, the newly translated scales were pretested on a small cohort of the target population to ensure their comprehensibility and make any final changes. These methods are echoed in the findings of a task force appointed by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR), which reviewed a number of methods employed by several organizations and distilled a 10-step set of guidelines for the translation and adaptation of patient-­reported outcome measures (Wild et al., 2005): (1) preparation, (2) forward translation, (3) reconciliation, (4) back translation, (5) back-­translation review, (6) harmonization among multiple language versions and the original instrument, (7) “cognitive debriefing” by testing the instrument on a relevant target group, (8) review of the cognitive debriefing results and finalization, (9) proofreading, and (10) producing a final report detailing the adaptation process. In their review of standards for the development of cross-­cultural quality-of-life instruments, Schmidt and Bullinger (2003) also add that the preparation stage should include literature review and focus groups with the aim of arriving at suitable test items, which are then pared down after pilot testing and cognitive debriefing. Additionally, the interval properties and item response characteristics of the resulting scales need to be ascertained, along with their psychometric properties of reliability and validity. Finally, these authors

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advocate norming the instrument with a representative sample of the target population. A great deal of literature already exists regarding the translation of psychological instruments, the details of which are beyond the scope of this chapter. The reader is referred to the International Test Commission (www.intestcom.org) and the associated International Journal of Testing (www.leaonline.com/loi/ijt) to keep abreast of developing guidelines on cross-­cultural test adaptation and administration, as well as discussions on statistical methods derived from item response theory, such as Rasch analysis (Lundgren-­Nilsson et al., 2005; Tennant, McKenna, & Hagell, 2004), designed to address the psychometric equivalence of adapted instruments. These issues are not covered in the present chapter, which instead focuses on construct validity.

Ascertaining Construct Validity Construct validity is paramount in the application of instruments that assess daily functioning. If our goal is to determine the level of specific functional abilities, say, for vocational placement, then we would be interested in knowing whether a person has the requisite skills in an absolute sense. In such a case, cultural differences are not of interest. For example, does the person have sufficient manual dexterity and visuospatial skills to work in an assembly line? The criterion for what constitutes sufficient ability will be indexed by the specific requirements of the job and (ideally) by the minimum level of ability of others already performing that job successfully. On the other hand, if we are interested in understanding whether someone with acquired cognitive deficits is suffering declines in his or her ability to live independently, then we need methods for capturing everyday functioning that are culturally and sociodemographically relevant for that individual. In their adaptation of the Functional Assessment of Chronic Illness Therapy (FACIT), Lent, Hahn, Eremenco, Webster, and Cella (1999) suggested five components of instrument equivalency: (1) semantic: the meaning of stimulus items is the same; (2) content: the items’ relevance to each culture is intact; (3) concept: the items measure the same theoretical construct; (4) criterion: the adapted and original items show similar properties when compared against a standardized measurement; and (5) technical: the method of assessment results in comparable cultural measurement. A variation of this scheme refers to component 4 as “item equivalence” and divides component 5 into “operational equivalence,” referring to the comparability of the measurement methods across cultures, and “measurement equivalence,” referring to the interpretability of results across cultures (Schmidt & Bullinger, 2003). In a similar vein, a cross-­cultural applicability research (CAR) effort led by the World Health Organization (WHO) and the U.S. National Institutes of Health (NIH) addressed both the cultural relativity of disability constructs and the psychometric requirements for the development of cross-­cultural instruments to measure disability and adaptive functioning (Üstün, Chatterji, Bickenbach, Trotter, & Saxena, 2001). This group focused on obtaining equivalency in three dimensions for a revision of the WHO International Classification of Impairments, Disabilities, and Handicaps (ICIDH; WHO, 1980), now called the International Classification of Functioning, Disability,

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and Health (ICF; WHO, 2001). The dimensions identified were (1) functional equivalence: the degree to which domains of activities can be identified that serve similar functions across different cultures; (2) conceptual equivalence: whether concepts of disability are understood similarly across cultures; and (3) metric equivalence: the degree to which measured constructs exhibit similar measurement characteristics in different cultures. In order to arrive at these components of cultural applicability, CAR investigators from 15 different countries attempted to identify (1) whether the domains, subdomains, and individual items of the original English-­language instrument corresponded to concepts in the local culture; (2) whether the domains, subdomains, and individual items were readily translatable, or whether a new English term needed to be adopted to facilitate translation; (3) whether the instrument’s components were applicable across sociodemographic groups within a culture; and (4) whether the instrument fit the needs and practices of institutions in the culture. Although the language used to describe components of instrument adaptation differ somewhat across authors, all point to ascertaining construct validity by ensuring that instruments applied cross-­culturally make sense linguistically, are conceptually understood, and have practical relevance in the culture, starting with whether the assessment process itself is comprehended in the culture. In large measure these aspects of instrument construction apply not only to determining equivalency between an existing instrument and its cross-­culturally adapted counterpart, but also when attempting to construct new instruments to measure adaptive functioning in a particular cultural context.

Linguistic Appropriateness One aspect of cultural and sociodemographic relevance, and among the first steps in the adaptation process, is accomplishing linguistic appropriateness. At its most basic, linguistic appropriateness requires that the words used in instructions and stimulus items be understandable by the person being evaluated. This requirement obliges those constructing or adapting a measure to be familiar with language use in the target population across educational level, social class, gender, geographic region, or any other stratification that may apply to that group. For example, the meaning of words can vary among Spanish speakers of different national origin. If a task required following instructions to bake a cake, the translation of “cake” for an Argentine population would be “torta.” However, this means “sandwich” in Mexico, so for that group the translation would have to be “pastel.” In addition, regional, educational, or social class differences in language use also typically exist within the same country. For instance, because the names of food and dishes are often regionally bound, it may be challenging to construct a linguistically neutral and generalizable activities of daily ­living (ADL) instrument that uses food-­related stimuli. This can also be the case with the names of medical conditions that a patient may be required to report. The case of food and illness names additionally illustrates possible influences of formal education and life experience within the same country or ethnic group, as it can be expected that individuals with greater education and affluence would be familiar with a broader range of food choices, formal medical terms, and other mainstream experiences. Thus, special care needs to be taken to accomplish translations and adaptations that are linguistically neutral and generalizable to as many

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variants of the target population as possible. Loewenstein and colleagues (Loewenstein, Arguelles, Barker, & Duara, 1993) give good examples of this process in their adaptation of neuropsychological and functional assessments for Spanish speakers in South Florida, where the investigators had to be aware of idioms that are prevalent, for example, among Spanish speakers of Cuban descent but unfamiliar to Hispanics from other parts of the world. For instance, the term “moros y cristianos” (Moors and Christians) is the name of a typical Cuban dish of rice and beans. In Spain, however, this term denotes the holiday commemorating the Reconquista, or the Moorish occupation of the Iberian peninsula that began in the 8th century and their eventual ouster by the Christians lasting through the 15th century.

Conceptual Equivalence The other challenge of linguistic appropriateness when adapting existing instruments is achieving conceptual equivalence in the translation. Often, words that correspond to a literal translation from the English do not convey the intended meaning. As an example, when translating the FACT, Bonomi and colleagues (1996) found that in the item “I am proud of how I am coping with my illness,” the expression of pride was viewed negatively by Norwegian respondents. As a result of input from physicians and patients, the phrase “proud of” was instead translated as the more acceptable “satisfied with.” Equally, certain concepts or expressions that are common in English may not have close equivalents in another language, such as the item “I am full of pep” from the Profile of Mood Scales (McNair, Lorr, Heuchert, & Droppleman, 1971). For the interested reader, the vicissitudes of achieving conceptual equivalence are demonstrated in the efforts to adapt the SF-36, which are detailed in a special issue of the Journal of Clinical Epidemiology (Wagner et al., 1998; www.SF-36.org). The SF-36 is the short form of the Medical Outcomes Study (MOS) Health Survey (Stewart, Ware, Sherbourne, & Wells, 1992), a self-­report health symptom inventory that has been translated for use in more than a dozen countries and multiple languages. During the process of adapting this questionnaire, teams of investigators in each country rated the difficulty of translating every item and offered their final wording for discussion within a panel of SF-36 experts to determine that conceptual equivalence was accomplished.

Ecological Validity As demonstrated by the WHO adaptation of the ICF (Üstün et al., 2001), conceptual equivalence does not apply only to language use, but is also dependent on conceptions of health and illness as well as mental and physical limitations across cultures. Thus, the construct validity of an instrument is threatened if it requires respondents to make judgments or attributions about their cognitive or physical capacities that they are not accustomed to making. Whether the instrument measures dependence in ADLs by self- or other-­report, clinician’s observation, or direct assessment of performance, the items being measured need to be representative of individuals’ experience in order to be meaningful. Therefore, in addition to the linguistic aspects mentioned, the construct validity of a measure of everyday functioning depends also on its ecological validity.

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In developing an ADL scale for use with Thai older adults with dementia, Senanarong and colleagues (2003) included certain culturally specific items that exemplify ecological validity, such as hiring a taxi-boat, bicycling, and walking to the village. Fillenbaum and colleagues (1999) similarly included culturally relevant components of daily functioning when creating an ADL scale for a rural older adult Indian population, such as the ability to remember important local festivals. Some examples of cultural differences in the relevance of items assessing everyday functioning were encountered by Jitapunkul, Kamolratanakul, and Ebrahim (1994) when attempting to use the Office of Population Censuses and Surveys (OPCS) England disability scale with an older adult Thai population. They noted that certain subscales of the OPCS resulted in extremely large proportions of disability in this group. In particular, the face validity of certain items such as “feels the need to have someone present all the time” and “sometimes sits for hours doing nothing” could not be interpreted in the same way as with English populations, since these can be normal aspects of Thai life. Additionally, certain items that were meant to assess basic ADLs in Western cultures corresponded to extended ADLs in Thai culture. For instance, “climbing a flight of stairs” is considered a basic ambulation activity in Western scales, but since traditional Thai homes contain difficult-to-­navigate ladders instead of stairs, this item needs to be considered an extended ADL. Ecological or face validity can require attention even when the adaptation is between relatively similar cultures. During the adaptation of the SF-36 International Quality of Life Assessment into Swedish, certain items from the original English­language version had to be changed to improve face validity. These included changing “playing golf” to “walking in the forest or gardening,” adapting the notion of “walking a block” to a distance in kilometers for rural populations, and noting that the effort and complexity of dressing oneself differs depending on the climate that is typical for that population (Wagner et al., 1998). To determine the ecological validity of items in measures of adaptive functioning, it is therefore also important to establish the degree of familiarity with the tasks or items to which a person is being asked to respond. This is critical when attempting to document declines in ADL independence and their relationship to cognitive functioning, as task familiarity is likely to affect responses independently of acquired neuropsychological impairment. For example, in certain traditional households, men across the socioeconomic spectrum may be unfamiliar with cooking or grocery shopping. The same may be true of women of very high socioeconomic status (SES) who might have service personnel to perform these tasks. Thus, persons fitting these descriptions might perform more poorly on a laboratory task of everyday functioning that requires preparing a meal, despite intact cognitive status. Similarly, very healthy people may have few opportunities to take medications; thus, sicker people may do better at a medication management task. SES could also influence performance on such a task, as indigent people may have had fewer opportunities to take medications. Another aspect of ecological validity pertains to familiarity with the manner in which responses are to be obtained. A clear and simple example of this is the inappropriateness of requiring an illiterate person to select a response from a number of written questionnaire items. A less obvious instance of adjustments required to maintain ecological validity is demonstrated by the work Baltussen and colleagues (Baltussen, Sanon, Sommerfeld, & Wurthwein, 2002), who found the need to adapt a

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visual analog scale (VAS) to measure burden of disease among low-­educated residents in rural Burkina Faso, West Africa. As the metric properties of the traditional VAS were unfamiliar in this population, the authors employed a finite number of wooden blocks with which respondents could express their valuation of a number of disease states. Finally, after considering linguistic appropriateness, conceptual equivalence, and ecological validity, the resulting instrument needs to be tested with a pilot sample from the target population to ensure that it is understood and received as intended. At this stage, additional adjustments can be made based on feedback from the respondents. Only then should the other psychometric properties of the final instrument be subjected to examination, such as criterion validity, internal consistency, and reliability. Figure 8.1 summarizes the goals and steps required for the successful adaptation of measures for use across diverse cultural or linguistic settings.

Experiences in Adapting Direct Observation Measures of Daily Functioning from English to Spanish Direct observation of ADL performance requires measuring everyday behaviors in the individual’s environment or recreating common activities in a laboratory setting. The latter is more amenable to standardization and quantification, making it more useful for research and outcomes-based clinical care. This section illustrates the application of the concepts discussed earlier by showing the process of adaptation of laboratory measures of daily functioning for use with Spanish speakers from the U.S.–Mexico border region. Faced with the need to evaluate functional status in monolingual Spanish speakers with HIV in San Diego, California, my colleagues and I at the HIV Neurobehavioral Research Center (HNRC) undertook the adaptation of a battery of tests of everyday functioning that has shown relationships to HIV-associated cognitive impairment among English speakers (Heaton et al., 2004; Marcotte et al., 1999). This battery assesses a number of instrumental activities of daily living (IADLs) by direct observation in the laboratory and also includes reports of everyday functioning outside of the laboratory, ranging from subjective ratings of disability and life quality to verifiable information such as automobile driving record. The direct assessment measures conducted in the laboratory include basic (e.g., identifying currency, making change) and advanced (e.g., paying bills, staying within a budget) financial management, grocery shopping, cooking, ordering and paying for a meal at a restaurant, medication management exercises, a manualized and computerized assessment of job aptitude. With the exception of tests borrowed from the Direct Assessment of Functional Status (DAFS—Loewenstein & Bates, 1992; Loewenstein, Rubert, Arguelles, & Duara, 1995), which were already available in Spanish, all of our functional measures were first translated into Spanish by a master’s level linguist and experienced psychometrist under the supervision of a bilingual neuropsychologist. Then they back-translated by another bilingual neuropsychologist. Next, the translated measures were circulated among Spanish speakers with neuropsychological experience (psychologists and psychometricians) from five different regions (Argentina, Colombia, Mexico, Puerto Rico, and Spain; these were selected by convenience, but additional input would be

GOAL CONSTRUCT VALIDITY OF ADAPTED INSTRUMENT Linguistic Appropriateness Semantic/ Content Equivalence

Ecological Validity

Conceptual Equivalence

Cultural Relevance of Assessment Method

Cultural Relevance of Assessment Items

METHOD ADAPTATION Back Translation or Reconciliation by Another Expert

Translation by Expert

Review for linguistic neutrality by additional native speakers Implement suggested adjustments

Pilot-test on target group

Establish psychometric properties

Determine population normative performance

FIGURE 8.1.  Schematic of the goals and methods for accomplishing appropriate cross-cultural and cross-linguistic adaptations. The goal is to accomplish construct validity by ensuring that adapted instruments are linguistically equivalent and have cultural relevance. The methodology to accomplish these goals requires adaptation by experts, which may include translation, with iterative adjustment and harmonization by additional experts as well as feedback from pilot testing. Psychometric properties and equivalence with the original instrument need to be established before the adapted instrument is applied. Normative performance in the population also needs to be determined to interpret performance in patient groups. 216

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sought if the measures were to be used in countries not represented) to elicit appropriate refinements so that we could make the final measures as linguistically neutral as possible. At this step in the adaptation we also made certain contextual changes to fit our target population, which, in this case were comprised Spanish-­speaking immigrants of Mexican origin. As a simple example, for a task that requires ordering a meal at a restaurant, we replaced the English menu items with items listed on the menu of an actual Mexican restaurant in the area. The next step in our adaptation process was to pilot the resulting measures on a group of Spanish-­speaking study participants to gather feedback about the quality of the translation as well as the ecological validity of the exercises and questionnaires in the battery. Based on this feedback, we made a number of modifications to the original measures in order to make the functional assessments more culturally relevant and appropriate. The modifications were designed to change the cultural context of the task without altering the requisite abilities (Rivera Mindt et al., 2003). For example, we discovered that few participants used checks or checkbooks in their daily lives; therefore, for a section on financial management, we changed the task such that “utility bills” were paid in cash rather than with checks, and the checkbook balancing task was replaced with having to figure the balance remaining on a phone card. For a cooking task, we learned from our pilot participants that few used a microwave oven, as was required in the original English-­language exercise. We therefore modified the task to use a hotplate as a stove top, and it had a positive reception. Although the details of the tasks were adapted, the calculations or abilities required to complete each exercise remained the same. Likewise, the scoring schemes and ranges of possible scores for the various measures were unchanged in order to preserve equivalence with the English versions, as much as possible, and facilitate comparisons. In certain clinical or research settings, it is of interest to identify economic losses associated with unemployment or job changes related to an illness or disability. Among immigrant or displaced populations, factors other than disability may account for changes to lower levels of vocational functioning. These might include lack of language proficiency, unavailability of documentation pertaining to professional qualifications or permission to work, and barriers to the transference of professional degrees and licenses obtained abroad. Thus, in our adaptation of a self-­report employment questionnaire that includes a complete work history, highest vocational attainment, earned income, and degree of responsibility at work, we make a distinction between employment in the United States versus in the country of origin. Additionally, we obtain the participants’ own assessments of whether they are employed in accordance with their capabilities, and if not, their perception of reasons for this. Because participants are likely to have different levels of familiarity with certain activities, each task in our battery is followed by a graded 5-point classification of familiarity to determine the frequency with which the task is encountered in daily life. This information can then be used to examine the influence of familiarity on task performance. Additionally, since our target population is an immigrant sample, we have also included a multidimensional acculturation scale (Hazuda, Haffner, Stern, & Eifler, 1988) to help discern the influence of acculturative factors on task performance. Such information helps to confirm the ecological validity of the battery in this population. In addition to the laboratory tasks, we also included self-­report measures

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of daily functioning, from which we can derive information on concurrent validity. As of the writing of this chapter, this validation and standardization study was still in the data-­gathering phase. The performance of healthy Spanish speakers is to be used to describe the psychometric properties of the battery, such as test–­retest reliability, and an HIV-positive group will serve to partially validate the battery by showing its sensitivity to HIV-associated dysfunction. Preliminary results suggest that performance on the resulting functional battery is related to cognitive status among Spanish speakers with HIV (Cherner et al., 2006; Suarez et al., 2008).

Conclusions Awareness of the need for culturally appropriate instruments is becoming widespread among the various disciplines that deal with measurement of disability and functional independence. Substantial gains have been made in the development of parameters to guide the adaptation and construction of instruments for use across diverse cultural contexts, all with the aim of creating measures that have sound construct and psychometric properties. A number of widely used instruments, primarily self­report questionnaires that survey ADLs within other aspects of disability and health functioning, have been systematically adapted for use in multiple linguistic and cultural contexts, and their psychometric properties have been investigated. Such work is invaluable because it allows for potential comparisons of the functional impact of various health-­related states across the world. A number of less well-known instruments has also been created for use with specific populations, with methodologies and validation procedures that are not as consistently described. In order to compare the relationship between cognitive status and everyday functioning in diverse populations, not only do the ADL measurements need to be standardized and subjected to psychometric rigor, but also neuropsychological tests need to be appropriately adapted and normed for the groups on which they will be used. Significant work remains in the area of direct assessment of function. This method has intrinsic appeal, as it yields the most proximal observation of actual ability. A number of standardized batteries exists (Moore, Palmer, Patterson, & Jeste, 2007), but very little has been reported on the applicability of these measurements in diverse populations, either across the world or among ethnocultural groups within the same country (Jeste et al., 2005; Loewenstein et al., 1992). With this type of assessment, it may be challenging to create instruments that are universally equivalent. There may in fact be few activities of everyday functioning that can be standardized across cultures or sociodemographic groups with vastly different daily living experiences, such as, say, Japanese business executives and indigenous residents of the Orinoco river region. Such cross-group comparisons may need to be restricted to populations with similar ranges of industrialization and literacy, but the challenge is open to tackle the creation of these kinds of measurements. In principle, it should be possible to arrive at conceptual-level categories of daily functioning (e.g., procuring nourishment, maintaining shelter, engaging in commerce) that could be agreed are universally applicable to humans. The WHO ICF (2001) is an example of such an attempt. In sum, the generation of culturally appropriate and equivalent instruments is possible and desirable for the purpose of comparing effects of interest across popula-

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tions. There will be cases where the human experience is so dissimilar that sufficient equivalence among instruments cannot be accomplished. Conceptually, however, ecologically valid methods can be devised to measure the functional impact of illness and brain dysfunction within a population, even when cross-group comparisons are challenging. Moreover, although this perspective remains to be tested empirically, I would like to suggest that direct assessment of functional abilities may be the best indicator of cognitive status in persons with little or no formal education, where our traditional neuropsychological tests may be less informative, as long as tasks can be designed that are ecologically valid for the individual.

Acknowledgments I wish to acknowledge the contributions of Daniel Barron, BS, Monica Rivera Mindt, PhD, Paola Suárez, MA, Margarita Padilla-Vélez, PhD, and Carolina Posada, BA, in the writing of this chapter.

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and cultural variation: The ICIDH-2 cross-­cultural applicability research study. In T. B. Üstün, S. Chatterji, J. Bickenbach, R. Trotter, R. Room, & S. Saxena (Eds.), Disability and culture: Universalism and diversity (pp. 3–19). Seattle, WA: Hogrefe & Huber for the World Health Organization. van der Geest, S., & Reis, R. (2002). Ethnocentrism and medical antrhopology. In S. v. d. Geest & R. Reis (Eds.), Ethnocentrism: Reflections on medical antrhopology (pp. x–23). Amsterdam: Aksant. Wagner, A. K., Gandek, B., Aaronson, N. K., Acquadro, C., Alonso, J., Apolone, G., et al. (1998). Cross-­cultural comparisons of the content of SF-36 translations across 10 countries: Results from the IQOLA Project. International Quality of Life Assessment. J Clin Epidemiol, 51(11), 925–932. Wild, D., Grove, A., Martin, M., Eremenco, S., McElroy, S., Verjee-­Lorenz, A., et al. (2005). Principles of good practice for the translation and cultural Adaptation Process for Patient­Reported Outcomes (PRO) Measures: Report of the ISPOR Task Force for Translation and Cultural Adaptation. Value Health, 8(2), 94–104. World Health Organization. (1980). International Classification of Impairment, Disability and Handicap: ICIDH. Geneva: Author. World Health Organization. (2001). International Classification of Functioning, Disability and Health: ICF. Geneva: Author. Retrieved from http://apps.who.int/classifications/ icfbrowser/.

PART II

Everyday Impact of Normal Aging and Neuropsychiatric Disorders

Chapter 9

The Impact of Cognitive Impairments on Health-­Related Quality of Life Robert M. Kaplan, Brent T. Mausbach, Thomas D. Marcotte, and Thomas L. Patterson

D

iseases and their consequent disabilities are important for two reasons. First, illness may cause premature death. Second, diseases may cause dysfunctions, as well as other symptoms, that lead to disabilities in an individual’s performance of usual activities of daily living. Biomedical studies typically refer to health outcomes in terms of mortality (death) and morbidity (dysfunction) and sometimes of symptoms (Kaplan, 1990). Cognitive dysfunction is an important form of disability that has been understudied in relation to quality of life. We focus on cognitive issues in this chapter. Although the effects of cognitive dysfunction can occur in many conditions, we focus on neurocognitive impairments resulting from human immunodeficiency virus (HIV), psychosis, and Alzheimer’s disease. Over the last 30 years, medical and health services researchers have developed new quantitative methods to assess health status. These measures are often called quality-of-life (QOL) measures. Since they are generally used exclusively to evaluate health status, we prefer the more descriptive term “health-­related quality of life” (Kaplan & Bush, 1982).

Measurement of Health-­Related QOL Figure 9.1 summarizes the number of papers on quality of life identified in PubMed between 1972 and 2007. In 1972, PubMed did not identify any publications under the QOL topic heading. However, over the next 35 years, the number of articles that use the QOL key word grew dramatically. In 2007, PubMed identified 7,551 such articles. In 1 year, from 2006 to 2007, the number of articles listed under the QOL keyword grew by 16% (or 1,046 articles).

225

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IMPACT OF AGING AND NEUROPSYCHIATRIC DISORDERS

8000 7000

Publications

6000 5000 4000 3000 2000 1000 0 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007

Year FIGURE 9.1.  Quality-of-life publications in PubMed between 1972 and 2007.

There are a least three reasons for measuring QOL in clinical studies. First, QOL measures are used to quantify the impact of a condition and to compare the effects of diseases with the consequences of other chronic medical problems. Second, these measures can be used to evaluate changes resulting from clinical intervention over the course of disease. Third, QOL measures are necessary as a central component of cost-­effectiveness analysis. A wide variety of measures has been used to quantify health-­related QOL (Bourbeau, Maltais, Rouleau, & Guimont, 2004; Dimitrov & Rumrill, 2005; Kaplan, Ries, Reilly, & Mohsenifar, 2004; Schmier, Halpern, Higashi, & Bakst, 2005). These measures are similar in that each expresses the effects of medical care in terms that can be reported directly by a patient. However, the rationales for the methods differ considerably.

Distinctions between Health QOL Measures Table 9.1 lists some of the many methods for evaluating QOL outcomes and makes several distinctions between measures. There are two major approaches to QOL assessment: psychometric and decision theory. The psychometric approach is used to offer a profile summarizing different dimensions of QOL. The best known example of the psychometric tradition is the Medical Outcomes Study 36-Item Short Form (SF-36; Ware & Gandek, 1998). The decision theory approach attempts to weight the different dimensions of health in order to provide a single expression of health status. Supporters of this approach argue that psychometric methods fail to consider that different health problems are not of equal concern. A minor itch is a symptom,

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227

for example, as is coughing up blood. However, the importance of a minor itch and coughing blood is not equal. Simple symptom counts may miss the severity or impact of more serious complaints. In an experimental trial using the psychometric approach, some aspects of QOL may improve whereas others get worse. For example, a medication might reduce coughing but increase skin problems or reduce energy. When components of outcome change in different directions, an overall subjective evaluation is often used to integrate the components and offer a summary of whether the patient is better or worse off. The decision theory approach provides an overall measure of QOL that integrates subjective function states, preferences for these states, morbidity, and mortality. In addition to the distinction between psychometric and decision theory approaches, measures can be classified as either generic (top of Table 9.1) or disease targeted (bottom of Table 9.1). Generic measures can be used with any population, whereas disease-­targeted measures are used with patients who have a particular diagnosis. TABLE 9.1.  Summary of Quality-of-Life Measures Used to Evaluate Outcomes in Adults Measure

Type

Purposes

SF-36 (Ware & Gandek, 1998)

Profile

Descriptive studies, clinical change

Sickness Impact Profile (SIP; Bergner, Bobbitt, Carter, & Gilson, 1981)

Profile

Descriptive studies, clinical change

Nottingham (Kaplan et al., 1998) Health Profile (NHP; Baro et al., 2006)

Profile

Descriptive studies, clinical change

Health Utilities Index (HUI; Feeny et al., 1999)

Decision theory

Descriptive studies, clinical change, cost effectiveness

EuroQol (EQ-5D; Kind, 1997)

Decision theory

Descriptive studies, clinical change, cost effectiveness

Quality of Well-Being Scale (QWB; Kaplan et al., 1989, 1998)

Decision theory

Descriptive studies, clinical change, cost effectiveness

Health and Activities Limitations Index (HALex; Gold, Franks, & Erickson, 1996)

Decision theory

Descriptive studies, clinical change, cost effectiveness

National Eye Institute–25 Item Visual Functioning Questionnaire (NEI-VFQ-25; Mangione et al., 2001)

Profile

Descriptive studies, clinical change studies in vision and eye disease

Heart Failure Symptom Check list (HFSC; Grady, Jalowiec, Grusk, White-Williams, & Robinson, 1992; Grady & Lanuza, 2005)

Profile/symptomspecific

Descriptive studies, clinical change studies in heart failure

St. Georges Respiratory Questionnaire (SGRQ; Jones, Quirk, & Baveystock, 1991)

Profile

Descriptive studies, clinical change studies in chronic lung disease

Generic measures

Disease-targeted measures

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IMPACT OF AGING AND NEUROPSYCHIATRIC DISORDERS

Finally, measures can be divided by their uses. Most measures can be used to characterize populations and to study clinical changes. However, only generic, decision theory–based measures can be used to evaluate cost effectiveness. This chapter concentrates on a generic decision-based method that has been applied in a variety of studies, and, in particular, we address the potential impact of neuropsychological dysfunction on QOL.

Quality of Well-Being Scale The general health policy model grew out of substantive theories in economics, psychology, medicine, and public health. This model includes components for mortality (death), morbidity (health-­related QOL), and time. The rationale for the model is that diseases and disabilities are important for two reasons. First, illness may cause the life expectancy to be shortened. Second, illness may make life less desirable at times prior to death (health-­related QOL) (Kaplan et al., 1995; Kaplan, Bush, & Berry, 1976; Kaplan & Groessl, 2002; Kaplan, Groessl, Sengupta, Sieber, & Ganiats, 2005; Kaplan & Ries, 2005). Central to the general health policy model is a general conceptualization of QOL. The Quality of Well-Being (QWB) Scale is one method of measuring QOL for calculations in the model. The QWB is a preference-­weighted measure combining three scales of functioning with a measure of symptoms and problems to produce a point-in-time expression of well-being that runs from 0 (for death) to 1.0 (for asymptomatic full function) (Kaplan, Ganiats, Sieber, & Anderson, 1998). The model separates health outcomes into distinct components. These are life expectancy (mortality), functioning (morbidity), preference for observed functional states (utility), and duration of stay in health states (prognosis). The morbidity component is the core of the QWB measures. In addition to classification into observable levels of function, individuals are also classified by their symptoms or problems. Symptoms, such as fatigue or a sore throat, might not be directly observable by others, whereas problems, such as a missing limb, might be noticeable by others. On any particular day, nearly 80% of the general population is optimally functional. However, over an interval of 7 days, only 12% experience no symptoms. Symptoms or problems may be severe, such as painful neuropathy, or minor, such as mild stomach discomfort after taking medication. In order to obtain preference weights for observable health states, peer judges place the observable states of health and functioning onto a preference continuum ranging from 0 for death to 1.0 for completely well (Kaplan, Bush, & Berry, 1979). A quality-­adjusted life year (QALY) is defined as the equivalent of a completely well year of life, or a year of life free of any symptoms, problems, or health-­related disabilities (Kaplan et al., 1976). The well-life expectancy is the current life expectancy adjusted for diminished QOL associated with dysfunctional states and the durations of stay in each state (Bush & Zaremba, 1971). The model quantifies the health activity or treatment program in terms of the QALYs that it produces or saves. The General Health Policy Model integrates components to express outcomes in a common measurement unit. Using information on current functioning and duration, it is possible to express the health outcomes in terms of QALYs. The model for point-in-time QWB is:

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229

QWB = 1 – (observed mobility × mobility weight) – (observed physical activity × physical activity weight) – (observed social activity × social activity weight) – (observed symptom/problem × symptom/problem weight) QALY = QWB × duration in years The net cost–­utility ratio is defined as Net cost Net QALYs

=

Cost of treatment – cost of alternative QALYsT – QALYdsC

where QALYsT and QALYsC are the QALYs produced by treatment (T) and control (C) groups, respectively.

Alternative Versions of the QWB There are currently two versions of the QWB, an interviewer-­administered version and self-­reported version. In 1996 the QWB was adapted to a self-­administered form (QWB-SA). The current version of the QWB-SA can be printed on two sides of a single page and takes about 10 minutes to complete (Kaplan et al., 1998). The development of new forms for the QWB has gone through several stages. First, a new list of symptoms and problems was developed. The current version of the QWB uses a list of 26 symptoms or problems; the QWB-SA has 59 symptoms. The improved symptoms assessment not only better reflects health status, it also more closely resembles a clinical review of symptoms, thus increasing the clinical utility of the QWB-SA. The QWB-SA has been shown to be highly correlated with the interviewer-­administered QWB and to retain the psychometric properties (Kaplan, Sieber, & Ganiats, 1997). The QWB-SA should provide a better representation of HIV signs and symptoms; in addition, QWB preference weights have been validated in HIV-infected populations. The QWB has been used in numerous clinical trials and studies to evaluate medical and surgical therapies in conditions such as chronic obstructive pulmonary disease (Kaplan, Atkins, & Timms, 1984), HIV (Kaplan et al., 1995; Kaplan, Patterson, et al., 1997), cystic fibrosis (Orenstein & Kaplan, 1991; Orenstein, Pattishall, Nixon, Ross, & Kaplan, 1990), diabetes mellitus (Kaplan, Hartwell, Wilson, & Wallace, 1987), atrial fibrillation (Ganiats, Palinkas, & Kaplan, 1992), lung transplantation (Squier et al., 1995), arthritis (Kaplan, Alcaraz, Anderson, & Weisman, 1996; Kaplan, Schmidt, & Cronan, 2000), end-stage renal disease (Rocco, Gassman, Wang, & Kaplan, 1997), cancer (Kaplan, 1993), depression (Pyne, Patterson, Kaplan, & Gillin, 1997; Pyne, Patterson, Kaplan, & Ho, 1997), and several other conditions (Kaplan et al., 1998). Furthermore, the method has been used for health resource allocation modeling and has served as the basis for an innovative experiment on the rationing of health care by the state of Oregon (Kaplan, 1994; Kaplan & California Policy Seminar, 1993). Studies have also demonstrated that the QWB is responsive to clinical change derived from surgery (Squier et al., 1995) and from medical condi-

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IMPACT OF AGING AND NEUROPSYCHIATRIC DISORDERS

tions such as rheumatoid arthritis (Bombardier et al., 1986), AIDS (Kaplan et al., 1995), and cystic fibrosis (Orenstein et al., 1990). General information about the QWB can be found at qwbsa.ucsd.edu.

The Relationship between Neurocognitive Dysfunction and Health-­Related QOL Generic QOL measures, such as the QWB, can be used with any patient group. Indeed such measures are now used to evaluate patients with nearly all medical conditions. In the following sections we offer three examples (HIV, schizophrenia, Alzheimer’s disease) relating cognitive functioning to general QOL in clinical populations.

HIV-Related Neurocognitive Impairment The diverse impacts of both HIV disease and its treatment require a general approach to assessment. Although there have been several previous attempts to evaluate QOL in HIV-infected patients, most focused only on psychological outcomes. A number of studies has attempted to characterize the health status and economic impacts of HIV infection, although we are aware of only a few studies that have applied general health-­related QOL scales (Lorenz, Cunningham, Spritzer, & Hays, 2006; Wu & Bailey, 1988; Wu, Hays, Kelly, Malitz, & Bozzette, 1997). There have been many studies on QOL in HIV (Crystal, Fleishman, Hays, Shapiro, & Bozzette, 2000; Cunningham, Crystal, Bozzette, & Hays, 2005; Lorenz et al., 2006), and many of these papers have appeared recently (Bolge, Mody, Ambegaonkar, McDonnell, & Zilberberg, 2007; Howland et al., 2007; Maserati et al., 2007; Preau et al., 2007; Protopopescu et al., 2007; Shalit, True, & Thommes, 2007). However, few of the studies use measures appropriate for the evaluation of new therapies, including highly active antiretroviral therapy (HAART) (Maserati et al., 2007). Aggressive new therapies require new approaches to evaluation because they may cause short-term reductions in QOL in order to enhance long-term survival. Few assessment methods consider the effects of treatment over the course of time.

Diverse Effects of HIV on Everyday Functioning We examined data from 400 HIV-positive and 114 HIV-uninfected men enrolled in a longitudinal study at the University of California, San Diego (UCSD) HIV Neurobehavioral Research Center (HNRC), a collaborative investigation of UCSD Naval Hospital in San Diego and the San Diego Veterans Affairs Medical Center. Demographic characteristics of the participants are summarized in Table 9.2. The controls were slightly better educated, and there were fewer African Americans in the control group. No member of the control group had an HIV-related diagnosis. All participants received a comprehensive medical, neurological, and neuropsychological evaluation (see Heaton et al., 1994, for details). The information from the neurological examinations (sensation, motor function, reflexes, alertness, and concentration) was grouped into summary clinical ratings for central nervous system (CNS) and peripheral nervous system functioning, ranging from 1 for unimpaired to

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231

TABLE 9.2.  Summary of Demographic Characteristics for Controls and HIV-Positive Participants Controls (n = 114)

HIV-positive (n = 400) p-value

Socioeconomic status (5-point scale, 1 lowest, 5 highest)

Mean 3.7

(SD) (1.1)

Mean 3.8

(SD) (1.0)

p < .6

Education (years)

Mean 14.8

(SD) (2.1)

Mean 14.1

(SD) (2.1)

p < .01

Mean 22,560

(SD) (20,785)

Mean 22,049

(SD) (16,047)

p < .9

(%) (0.3) (12.3) (0.5) (1.3) (5.3) (0.3) (2.0) (0.3) (1.3) (0.3) (76.5)

p < .01

Income (in U.S. dollars)

Ethnicity   American Indian   African American   Cuban   Filipino   Hispanic   Latin American   Mexican American   Asian   Pacific Islander   Puerto Rican   White

n 2 4

(%) (1.8) (3.5)

6

(5.3)

2

(1.8)

2 98

(1.8) (86.0)

n 1 49 2 5 21 1 8 1 5 1 306

Note. From Kaplan et al. (1995). Adapted with permission from the American Psychosomatic Society.

5 for high levels of impairment (Gulevich et al., 1992). In addition, the ratings of central (i.e., brain) impairment were studied. A psychiatric evaluation assessed emotional well-being both historically and currently using standardized measures, including the Profile of Mood States (POMS; NcNair, Lorr, & Droppleman, 1980) and the Beck Depression Scale (Beck, 1976).

Relation between Health-­Related QOL and Neurological and Functional Status in HIV Infection The QWB was associated with a variety of neurological ratings of CNS impairment. These ratings were provided by a clinical neurologist on the basis of structured neurological evaluations and nerve conductance studies. Higher ratings by the neurologist represent more impairment. There was a systematic relationship indicating that neurologist ratings of greater severity were associated with lower (closer to death) QWB scores (Figure 9.2). HIV infection may have a wide variety of effects upon everyday functioning. One of the most important impacts is the inability or diminished ability to maintain gainful employment. One study in the pre-HAART era suggested that transition from HIV asymptomatic to HIV symptomatic status was associated with an estimated reduction in the rate of employment from 74 to 26%. Among the employed asymptomatic subjects, 44% earned more than $30,000 per year, whereas only 21% of the employed patients with AIDS earned $30,000 or more (Wachtel et al., 1992).

232

IMPACT OF AGING AND NEUROPSYCHIATRIC DISORDERS 0.9 0.8

QWB

0.7 0.6 0.5 0.4 0.3

1

2

3

4

5

Ratings of Central Impairment FIGURE 9.2.  Relationship between neurologist global ratings of impairment and QWB scores. Based on Kaplan et al. (1995).

The disabling aspects of HIV disease become increasingly important as they affect work. In particular, it is important to allow the person with AIDS to continue to be productive in the workplace as long as he or she has the functional capacity to do so; some studies have indicated that once HIV-infected individuals stop working, they are unlikely to return to work (Rabkin, 2004). The HNRC study offered some of the first evidence suggesting that neurocognitive impairment is associated with employment status. Compared to cognitively normal HIV-positive participants, HIV-positive participants with neuropsychological impairment had higher rates of unemployment and subjective decreases in job-­related abilities and work productivity, even after controlling for medical status (Heaton et al., 1994). Figure 9.3 shows the differences between estimated wages for HNRC participants who were unimpaired versus those who were impaired. The relationship between neurocognitive functioning, employment, and the QWB is complex. Figure 9.4 summarizes the relationship between a neuropsychologist’s global rating of functioning and a QWB score, indicating that as impairment increases, QOL is reduced. In further pilot investigation, a random sample of 100 HNRC participants was evaluated for recent changes in work status. The purpose of the study was to find real-life correlates of neuropsychological deficits. A group of 11 had a recent reduction in work time or had lost a job. These 11 were not different from the rest of the cohort on their most recent neuropsychological or blood chemistry measures. There were, however, significant differences on the QWB, but in the unexpected direction. Those who had reduced work or lost a job scored higher in QOL at the 1-year follow-up than the cohort as a whole (.850 vs. .735; p < .003) and

Health-­Related Quality of Life

233

425

Mean weekly wages

415 410 405 400 395 390 385 380

HIV–

HIV+ unimpaired Group

HIV+ impaired

FIGURE 9.3.  Differences between estimated wages for HNRC patients who were unimpaired versus those who were impaired. Error bars not shown because data are estimates.

0.79 0.78

QWB

0.77 0.76 0.75 0.74 0.73 0.72 None

Mild impairment

> Mild

Impairment FIGURE 9.4.  Relationship between a neuropsychologist’s global rating of functioning and QWB score.

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IMPACT OF AGING AND NEUROPSYCHIATRIC DISORDERS

had improved QWB scores over the last year (+ .063), whereas the total cohort had declined slightly (–.009; p < .07). These findings may suggest that reducing demands associated with work may have some health benefits. We are hesitant to place much importance on these findings because there were only 11 patients in this group, and we are unable to determine whether other clinical or demographic characteristics might explain the differences in the QWB scores (Pyne et al., 2003).

Relationship between QOL and Psychological Functioning in HIV Infection A variety of studies has demonstrated the validity of the QWB for assessing depression in patients with HIV disease. In one study ratings of depression using the Hamilton Depression Scale (HAM-D) were obtained from 285 HIV patients and 84 HIVnegative men who participated in the HNRC cohort. The data were obtained at baseline and 6 months later. Depression was defined as HAM-D scores greater than 10. The study demonstrated a systematic relationship between HAM-D scores and QWB scores at baseline (t = 8.74, p < .001). In addition, 22 HIV-positive subjects experienced increases of 10 points on the HAM-D Scale between 1- and 6-month evaluations. Significant reductions in QWB scores were observed for these individuals (t = 2.62, p < .02). Analyses of QWB symptoms suggested greater symptom severity among those whose HAM-D scores increased (Kaplan et al., 1997). In addition, there was a greater reduction in physical activity component of the QWB among those experiencing an increase in depression. In other words, mood was associated with both symptoms and physical function. One interpretation is that the physical symptoms of HIV/AIDS prevent or restrict people from engaging in preferred activities, which in turn causes depression. This model is supported among persons undergoing chemotherapy or radiation therapy for cancer (Williamson, 2000).

HIV-Associated Neurocognitive Disorders and Health-­Related QOL HIV-associated neurocognitive disorders (HAND) can range from subtle, “asymptomatic” deficits that do not affect everyday functioning to HIV-associated dementia (HAD), a severe and debilitating dementia that significantly affects activities of daily living (Antinori et al., 2007; Grant & Atkinson, 1995). In 2006 a workgroup (“Frascati Workgroup”) was organized by the National Institute of Mental Health and National Institute of Neurological Diseases and Stroke, with the goal of revisiting the diagnostic criteria originally proposed by an American Academy of Neurology AIDS Task Force (American Academy of Neurology AIDS Task Force, 1991) to determine whether the criteria should be revised (based on clinical research and possible phenotypic changes seen over the last 15 years). The final criteria proposed by this group (Antinori et al., 2007) were consistent with those originally proposed by Grant, Heaton, and Atkinson (1995) and in use at the HNRC. In order to examine the relationship between neurocognitive status and health­related QOL, we classified HIV-positive study participants into diagnostic categories consistent with the schema proposed by the Frascati Workgroup. To be considered “impaired,” individuals must show cognitive impairment in at least two cognitive domains, such that a focal deficit in a single cognitive ability domain does not qual-

Health-­Related Quality of Life

235

ify as having “global” neurocognitive impairment. The primary criteria for determining whether an individual has a HAND (1) the presence of neuropsychological impairment and (2) whether these impairments impact everyday activities. In brief, a diagnosis of asymptomatic neurocognitive impairment (ANI) involves the presence of cognitive impairment, objectively determined via cognitive tests, which does not affect everyday functioning. Mild neurocognitive disorder (MND) requires objective evidence of neuropsychological impairments that cause noticeable difficulty in the execution of everyday activities. The criteria for HAD are similar, but require more severe cognitive impairment and marked disruption in everyday functions. In each of these diagnostic categories, the impairment, in the opinion of the diagnosing clinician, cannot be attributable to a comorbid condition and must represent a decline from previous functioning. Figure 9.5 shows the relationship between diagnostic classification and QWB score. As the figure shows, the impact on health-­related QOL is greatest in those who meet criteria for syndromic impairment; MND and HAD.

Self-­Reported Cognitive Functioning and QWB in HIV Infection Participants also completed the Patient’s Assessment of Own Functioning Inventory (PAOFI; Chelune, Ferguson, Koon, & Dickey, 1986), a 41-item questionnaire in which participants report the frequency with which they experience problems with memory, language/communication, use of hands, sensory perception, and higher-level cognition. The questionnaire focuses on cognition. As seen in Figures 9.6–9.8, health­related QOL decreases as the number of cognitive problems increases. ­Figure  9.6

0.8 0.75

QWB

0.7 0.65 0.6 0.55 0.5

0

1

2

3

4

5+

AAN Diagnosis FIGURE 9.5.  QWB by American Academy of Neurology diagnosis.

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IMPACT OF AGING AND NEUROPSYCHIATRIC DISORDERS

0.75

QWB

0.7

0.65

0.6

0.55

0.5

zero one Patient Assessment of Motion Functioning

two

FIGURE 9.6.  QWB patient assessment of motion functioning.

0.8 0.75

QWB

0.7 0.65 0.6 0.55 0.5

0

1

2

3

4

5

Patient Assessment of Language FIGURE 9.7.  QWB by patient assessment of language.

6+

Health-­Related Quality of Life

237

0.8 0.75

QWB

0.7 0.65 0.6 0.55 0.5

0

1

2

3

4

5

6

7

8

9

10

11

12 13+

Patient Assessment Total FIGURE 9.8.  QWB by patient assessment total.

shows the relationship between self-­reported motor functioning (difficulty feeling objects with one’s hands and seeing things) and QWB scores. As seen in the figure, there is a systematic relationship between problems in perceptual–motor functioning and QWB, in that participants with two or more problems lose more than 15% of their QOL. Figure 9.7 shows the relationship between language problems and QWB. Language was assessed using 10 items that asked about difficulties in understanding language, spoken communications, reading, and speaking, as well as thinking of names, retrieving names, writing, and spelling. There was a systematically negative correlation between these variables. Patients with no problems in language achieved QWB scores of nearly 0.75. As language problems increased, QWB scores decreased. For those reporting six or greater language problems, QWB scores were 0.60. In other words, there was nearly a 0.15 difference compared to those without problems. For each 6 years a person lives with a 0.15 deficit in self-­assessed language, the equivalent of 1 year of life is lost. Overall, there were 5 self-­assessment scales and 33 individual items on the PAOFI. Figure 9.8 shows the relationship between total number of self-­assessed problems and QWB scores. Those with no self-­assessed problems have QWB scores of 0.77, whereas those with 13 or more self-­assessed problems have QWB scores of only 0.57.

Relationship between Neurocognition and Overall Functioning in Middle-Aged and Older Adults with Psychosis Atypical antipsychotic medications may have an important role in the management of symptoms in patients with schizophrenia. The overall importance of schizophrenia

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IMPACT OF AGING AND NEUROPSYCHIATRIC DISORDERS

deserves attention because the disease is expensive in terms of treatment costs, loss of productivity, and public assistance expenditures (Ganguly, Kotzan, Miller, Kennedy, & Martin, 2004; Miller & Martin, 2004). Improved care of schizophrenia symptoms has resulted in a greater number of patients living in the community rather than in institutions. However, reductions in symptoms do not automatically result in an improved ability to function in the real world. In order to assess these issues, we used data from a specialized center at UCSD and the San Diego Veterans Affairs Medical Center. The center focuses on late-onset schizophrenia. We conducted an analysis of the relationship between cognitive functioning and QWB scores among 240 middle-aged and older adults (mean age = 52.6 ± 7.4 years; range = 37–79 years) with schizophrenia or schizoaffective disorder. To be eligible, patients had to be 40 years or older and have a DSM-IV-based diagnosis of schizophrenia or schizoaffective disorder. Patients were excluded if they (1) had a DSM-IV diagnosis of dementia, (2) represented a serious suicide risk, (3) could not complete the assessment battery, or (4) were participating in any other psychosocial intervention or drug research at the time of intake. Participants were primarily male (64.4%) and residing in an assisted-­living setting such as a board and care, nursing facility, or skilled nursing facility (71%). The remainder of participants resided in the community either alone (9%) or with a friend or family member (20%). At the time of their assessments, all participants were receiving medication treatment with typical and/ or atypical neuroleptics. Functional outcomes were assessed using three measures. The first, called the UCSD Performance-Based Skills Assessment (Mausbach et al., 2007), requires participants to role-play a variety of complex situations involving management of finances, social and communications skills, transportation, and household chores. Participants are given a score in each functional area, and the sum of scores from each domain is the total score. Higher scores indicate better functioning. Global cognitive functioning was assessed using the Dementia Rating Scale (DRS). Symptoms of psychosis were assessed via the Positive and Negative Syndrome Scale (PANSS). An analyses of 236 participants revealed a significant correlation between total DRS scores and the QWB scores (r = .25, p < .001), with lower QWB scores associated with worse cognition. Mean QWB scores by tertile of DRS are shown in Figure 9.9. The QWB was significantly correlated with four of the DRS domains: attention (r = .17, p = .009), initiation (r = .24, p < .001), conceptualization (r = .14, p = .032), and memory scores (r = .22, p = .001). The correlation between DRS construction scores and QWB scores was not significant (r = .07, p = .3). Figure 9.10 shows QWB scores by tertiles of the initiation component of the DRS. As with the total score, there is a strong relationship between initiation–­preservation scores and QWB.

Estimating the Overall Impact of Neurocognitive Consequences in Alzheimer’s Disease Alzheimer’s disease is a degenerative brain disorder that results in gradual atrophy of higher cortical regions. It is marked by gradual onset and a deteriorating course. The first symptoms of Alzheimer’s disease include forgetfulness, anomia, irrationality,

Health-­Related Quality of Life

239

0.58 0.57 0.56 0.55

QWB

0.54 0.53 0.52 0.51 0.5 0.49 0.48

1

2

3

Tertile FIGURE 9.9.  Mean QWB scores by tertile of Dementia Rating Scale in patients with late-onset schizophrenia.

0.57 0.56 0.55

QWB

0.54 0.53 0.52 0.51 0.5 0.49

1

2

3

Tertile FIGURE 9.10.  Mean QWB scores by tertile of the initiation component of the Dementia Rating Scale in patients with late-onset schizophrenia.

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IMPACT OF AGING AND NEUROPSYCHIATRIC DISORDERS

loss of initiative, and disorientation. These initial symptoms progress to widespread dementia, loss of functioning, and death. An amnesic syndrome is most prominent in many patients with Alzheimer’s disease; in others, naming and spatial difficulties are primary (Loewenstein, Acevedo, Agron, Martinez, & Duara, 2007). Alzheimer’s disease is more common than previously believed, affecting about 15% of adults over age 64 and contributing significantly to health care costs (Kirby et al., 2006). In the general population, this risk rises to 25% at age 90. In first-­degree relatives of patients with Alzheimer’s disease, the risk at age 90 is 50% (Breitner, Silverman, Mohs, & Davis, 1988). We have attempted to quantify the impact of Alzheimer’s disease. In contrast to diseases that cause early death, those affected by Alzheimer’s disease gradually lose function over an extended period of time. Focusing only on mortality fails to recognize the serious impact Alzheimer’s disease has upon health-­related QOL. In order to understand the full impact of Alzheimer’s disease, it is necessary to develop models that consider the effects on both mortality and life quality. Similarly, new interventions developed for the treatment of Alzheimer’s disease need to be evaluated with measures that consider side effects as well as the benefits. To evaluate this issue we studied 159 patients with the diagnosis of probable or possible Alzheimer’s disease and their spousal caregivers, along with 52 control nonpatient–­spousal dyads (N = 211) recruited as part of a longitudinal study on Alzheimer’s disease caregiving. In addition to the QWB, the subjects completed several other measures. The Mattis Dementia Rating Scale (MDRS; Miller & Pliskin, 2006) consists of a mental status examination that assesses basic cognitive functions such as recent and remote memory, attention, orientation, mental control, and language. The Memory and Behavior Problem Checklist (MBPC; Zarit, Reever, & Bach-­Peterson, 1980) consists of 29 common problems encountered in dementia. The spousal caregiver (or, in the case of controls, the spousal noncaregiver) completed these items by proxy for the reference subject. This measure assesses both the frequency of the dementia problems in the patient and the degree of stress that the caregivers experience. The Brief Symptom Inventory (BSI) is a 53-item self-­report inventory taken from the Hopkins Symptom Checklist (Derogatis, Yevzeroff, & Wittelsberger, 1975). It measures five dimensions of psychiatric symptom distress that are commonly associated with Alzheimer’s disease: anxiety, depression, obsessive–­ compulsiveness, somatization, and interpersonal difficulty. Measures of respite time taken by patient caregivers were also obtained. “Respite time” refers to the amount of relief that caregivers took from the burden of caring for the patients with Alzheimer’s disease. Respite time taken may increase as the disease progresses, making it an indirect measure of the severity of the disease. However, other factors, such as the health of the caregiver and the availability of resources, also may influence the amount of respite time taken. Not much is known about the reliability and validity of respite time as a marker of disease progression in Alzheimer’s disease. Therefore, results concerning a relationship between respite time and the QWB must be interpreted with caution. Parametric tests were used to evaluate the relationships between the QWB and the other measures. Subjects who completed each measure of interest were included in the analysis. Scores on the QWB were found to be strongly associated with measures of different aspects of impairment resulting from Alzheimer’s disease.

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Patients with poorer cognitive functioning in areas such as recent and remote memory, attention, orientation, mental control, and language, as measured by the MDRS, tended to have lower QWB scores (r = .52, p < .01). Lower QWB scores were also associated with greater behavioral impairment, as measured by the MBPC (r = .64, p < .01). Strong relationships were found between the QWB and MBPC items. Mean QWB scores were calculated for each of the five MBPC response options, and the means were compared by analysis of variance. Differences were found for many items, such as Asks Repeatedly (F(4,123) = 15.49, p < .001), Forgets Day (F(4,122) = 36.43, p < .001), Loses Things (F(4,125) = 11.32, p < .001), Unable to Cook (F(4,124) = 10.54, p < .001), Unable to Shop (F(4,123) = 10.23, p < .001), and Unable to Do Simple Tasks (F(4,124) = 13.27, p < .001). The correlations obtained were highly significant: lower QWB scores were associated with poorer reported functioning (see Figure 9.11). Follow-up analyses indicated that in most cases, the significant results obtained were a reflection of large differences in QWB between items scored as “never” or “not at all” and items indicating that the poorer functioning was present to at least some degree. QWB scores were also associated with self-­reported psychiatric distress: The relationship between the QWB and the BSI, although weaker than the relationship between the QWB and the measures of cognitive and behavioral functioning, was nevertheless still statistically significant (r = –.26, p < .01). Caregivers of patients with lower QWB scores received a greater amount of respite time (F(4,123) = 25.26, p < .01) and needed it more often (F(4,108) = 13.42, p < .01) than did caregivers of patients with higher QWB scores It might be argued that the analyses were biased because some nonaffected patients were included. Contrasts between patients and controls provide estimates of the impact of disease in comparison to persons roughly matched by age, sex, socioeconomic status, and living conditions. For a more stringent test we compared QWB scores against MBPC scores, with analysis limited to those carrying a diagnosis of Alzheimer’s disease. For this analysis there were significant linear contrasts for several MBPC scales, including Unable to Dress (F(1,95) = 4.65, p < .03), Unable to Feed (F(1,95) = 3.87, p < .05), Unable to Bathe (F(1,95) = 3.63, p = .05), Unable to Shave (F(1,95) = 6.16, p < .01), Incontinent (F(1,95) = 9.23, p < .01), and Unable to Cook (F(1,95) = 6.79, p < .01). Results from this study suggest that the general QWB is significantly associated with measures of dementia, memory and behavior problems, psychiatric symptoms, and respite time. These variables were selected for study because of their presumed relationship with QOL (Kerner, Patterson, Grant, & Kaplan 1998).

Summary Cognitive dysfunction can significantly impact many aspects of everyday functioning, and as such can also profoundly affect overall QOL. Data from a variety of studies at UCSD have demonstrated a systematic relationship between cognitive dysfunction and overall health-­related QOL. There is a variety of different ways to measure health-­related QOL. This chapter has focused on a general method known as the QWB scale. This is one of several approaches to estimate health outcome on a con-

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IMPACT OF AGING AND NEUROPSYCHIATRIC DISORDERS QWB by Losing Things

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It is also notable that we have not included a detailed discussion of bipolar disorder in this chapter. There is growing evidence that individuals with bipolar disorder have significant cognitive impairments and that the level of impairment may be more severe and more diffuse than that seen in persons with MDD, but less severe than the cognitive deficits observed in persons with schizophrenia (Bearden, Hoffman, & Cannon, 2001; Depp et al., 2007; Dickerson et al., 2004; Gildengers et al., 2007; Lingam & Scott, 2002; Revicki, Matza, Flood, & Lloyd, 2005; Robinson & Ferrier, 2006; Robinson et al., 2006; Sarkisian et al., 2000; Savitz, Solms, & Ramesar, 2005a, 2005b). Furthermore, there is emerging evidence that these NP impairments in persons with bipolar disorder may relate to difficulties in everyday functioning (Dickerson et al., 2004; Martinez-Aran et al., 2007). Indeed, the evidence is strong that neurocognitive deficits in persons with bipolar disorder are present outside of affective episodes, whereas the evidence for such deficits among persons with MDD is less clear.

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Prevalence of Depression Depression is extremely common; the National Comorbidity Study (NCS) estimates that 21.2% of American adults will have at least one depressive episode in their lifetime (Kessler et al., 2008). Although MDD is more common among women as compared to men, the disorder affects all genders, ages, and races (Kornstein et al., 2000). Research suggests a general underdiagnosis of MDD, especially for patients who have comorbid physical illnesses such as asthma or diabetes, probably because both patients and physicians attribute depressive symptoms to medical causes (Moussavi et al., 2007). As of the year 2000, it was estimated that depression cost the United States $83 billion annually for, among other factors, health care costs and hours of work lost (Greenberg & Birnbaum, 2005). Furthermore, depression was the fourth leading source of the global burden of disease among all diseases and disorders, as measured by disability adjusted life years (DALYs), and the leading cause of disability when measured by years living with disability (YLDs) in the year 2000 (Ustun, Ayuso-­Mateos, Chatterji, Mathers, & Murray, 2004). For younger persons (those ages 15–44) depression is the second leading source of disease burden. As the public becomes more informed in recognizing depressive symptoms, by the year 2020 MDD may be recognized as the second strongest contributor of lost years regardless of sex or age (Murray & Lopez, 1996). The course of depressive illness can be quite variable. For instance, some individuals have severe depression that is treatment resistant, whereas others respond well to treatment. Even at subsyndromal levels, depression seems to have a significant effect on daily functioning and may cause particular difficulties with psychosocial functioning (Judd et al., 2002). Individuals with depression can experience mental, role-­emotional, and social dysfunction that is at least as debilitating as serious medical conditions such as coronary artery disease, hypertension, and chronic back pain (Wells & Sherbourne, 1999). To highlight the fact that disability is not just the result of a medical or biological dysfunction, the World Health Organization revised its International Classification of Functioning, Disability, and Health (ICF) in 2001, thereby strengthening the notion that disorders such as MDD should be placed on an equal footing with all health conditions (resolution World Health Assembly 54.21). Because the lifetime risk of depression is 5–10 times greater than that of many medical conditions, at a time when severe medical illness is unlikely, it may be more debilitating on a longterm and population basis. This seriousness of disability is compounded by underrecognition, consequent lack of treatment for MDD, and the fact that those in the young-to-­middle age range are particularly vulnerable to this disorder. A recent study of 240,000 people in 60 countries showed that depression alone is more debilitating than chronic physical diseases, including asthma, angina, arthritis, and diabetes. Moreover, patients with the burden of such physical diseases have an increased risk of depression, and, not surprisingly, those with both MDD and a physical disease have lower health scores than those with physical health problems alone (Moussavi et al., 2007). Despite the impact of depression on daily functioning, there has been a relative dearth of research focused on its cognitive and everyday consequences. This lack of focus on daily functioning is likely a result of several factors: (1) There are numerous

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efficacious treatments for depressive symptoms, and it is often assumed that cognitive and daily functioning problems will improve simultaneously with treatment of the underlying mood disturbance; (2) the severity of depressive symptomatology and functional disability can vary greatly; and (3) everyday functioning is difficult to measure, and a consensus definition for what is deemed impairment in daily functioning has not been formulated.

Treatment of Depression Although numerous efficacious treatments for MDD exist, many individuals go undiagnosed and untreated (Gelenberg & Hopkins, 2007). Even with optimal pharmacological treatment, a significant proportion of treated persons (20–40%) continue to experience depressive symptoms (Keitner, Ryan, & Solomon, 2006). There are likely different cognitive and functional outcomes for those individuals who are treated, those who are not, and those that have failed treatment. Likewise, the treatment outcome for patients with comorbid medical issues and depression requires that both mental and physical health issues be addressed. Within this group of patients, treatment is often focused on more obvious physical diseases while depression is left unaddressed (Andrews & Titov, 2007). Taking this requirement into consideration, it is important to consider the implications of the various treatment approaches for resolution of mood, impact on cognition, and effect on everyday functioning. Below we examine both psychopharmacological and psychosocial treatments for depression.

Psychopharmacological Treatments for Depression Psychopharmacological treatments for depression are widely used. In fact, a recent report by the Centers for Disease Control and Prevention showed that antidepressant medications are the most frequently prescribed medications in the United States (Burt, McCaig, & Rechtsteiner, 2007). More recently developed antidepressant medications (e.g., selective serotonin reuptake inhibitors [SSRIs] and related compounds) have better side effect profiles than earlier medications (e.g., tricyclic antidepressants [TCAs]), and SSRIs are commonly prescribed for the treatment of depression in the United States. A review examining 108 meta-­analyses of the efficacy of antidepressant medications for the treatment of depression revealed that older antidepressants (e.g., those developed prior to the early 1980s) were equally efficacious as newer antidepressant medications. Study findings also revealed superior efficacy of serotonin and noradrenalin reuptake inhibitors (SNRIs) and a greater tolerability of SSRIs as compared to TCAs (Anderson, 2000). Finally, long-term maintenance treatment with an antidepressant medication seems important as it has been shown to provide better outcomes for individuals with depression than brief, short-term psychopharmacological intervention (Chisholm, Saxena, & van Ommeren, 2006). The effect of pharmacological treatment on cognitive functioning has not been well established due to difficulties in disentangling the contribution of the depressive symptoms and medications. Some studies have suggested that SSRIs may negatively influence general memory abilities, resulting in forgetting, for example (Goldstein &

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Goodnick, 1998; Joss, Burton, & Keller, 2003). However, the design of these studies does not allow for the disorder and treatment to be teased apart. In one study, relatively young, working individuals taking SSRI medications preformed worse on recognition and delayed recall tasks as compared to nondepressed individuals who were not taking SSRIs. However, depressive symptoms among the individuals taking the SSRIs had not yet resolved, so it is unclear if the impairments were a result of treatment or depression (Wadsworth, Moss, Simpson, & Smith, 2005).

Psychosocial Treatments for Depression Several structured psychotherapeutic interventions have also been shown to produce substantial improvements in both mood and quality of life. Specifically, cognitive­behavioral therapy (CBT) and interpersonal therapy (IPT) have been shown to be effective in the treatment of depression. CBT is a structured therapy that can be given over a short time period. CBT focuses on the here and now and seeks to change maladaptive thoughts that may negatively affect behavior. IPT also focuses on current difficulties but in relation to interpersonal relationships, rather than maladaptive thoughts, under the premise that depressive symptoms will decrease with the resolution of interpersonal problems. The cost efficacy of psychotherapy is comparable to that for treatment with generic antidepressants, but it is less widely available (­Chisholm et al., 2006). Both psychopharmacological and psychosocial approaches to the treatment of depression appear to improve overall quality of life, including sleep, concentration, interpersonal functioning, and energy. Initial treatment with antidepressant medications and/or an indicated psychosocial intervention can lead to significant gains in terms of more years of healthy life, and long-term maintenance treatment leads to even higher gains (Chisholm et al., 2006). Furthermore, maintenance treatment with an antidepressant medication has been shown to decrease risk for relapse (Geddes et al., 2003). Antidepressants and therapy have been shown to be particularly effective in decreasing the number of days depressed (Malt, Robak, Madsbu, Bakke, & Loeb, 1999; Solomon et al., 1997).

Neurobiology/Neuroanatomy of Depression Catecholamine Hypothesis Conventional hypotheses regarding the neurobiological underpinnings of depression suggest that abnormal levels of monoamine neurotransmitters are associated with the illness. The so-­called “catecholamine hypothesis” suggests that individuals with depression have depleted levels of several neurotransmitters, particularly serotonin and norepinephrine (Schildkraut, 1965). As a result, many antidepressant medications seek to boost the availability of these neurotransmitters either by facilitating their release into the synaptic cleft or by blocking their reuptake. Although the monoamines clearly have a large role in the development, and consequently the treatment, of depression, more recent hypotheses show that there are numerous other factors related to depression and that the underlying biology of this disorder is complex (Nemeroff & Vale, 2005; Owens, 2004).

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A description of the underlying neurobiology of depression would be incomplete without mentioning the hypothalamic–­pituitary–­adrenal (HPA) axis, which plays a role in emotional behavior and is responsible for regulating stress. The HPA has been found to be dysregulated in persons with mood disorders and may serve to identify persons who may be at risk for development of serious depressive symptoms (Brown, Varghese, & McEwen, 2004; Goodyer, Herbert, & Tamplin, 2003; Varghese & Brown, 2001).

Neuroimaging Evidence of Brain Systems Involved in Depression Neuroimaging studies have generally shown both structural and functional abnormalities in the frontal lobes and basal ganglia of persons with depressed mood. The specific neuroanatomical regions identified via structural neuroimaging of persons with depression reveal increased white matter abnormalities and decreased size of both the frontal lobes and the caudate nucleus as compared to persons without depression (Kanner, 2004; Krishnan et al., 1992; Sheline, 2003; Soares et al., 2001). Functional neuroimaging of people with depression shows decreased cerebral blood flow and cerebral metabolism in the inferior frontal, dorsolateral prefrontal, and anterior cingulate regions of the frontal lobe (Bench et al., 1992; Deckersbach, Dougherty, & Rauch, 2006; Fitzgerald et al., 2006; Lesser et al., 1994; Mayberg, 1994). There is evidence that the ventromedial prefrontal cortex is particularly affected in persons with unipolar depression and that damage to this region may be involved in emotional processing (Damasio, 1997). Similarly, Chamberlain and Sahakian (2006), in a review of the NP of mood disorders, have suggested that the orbital frontal and anterior cingulate regions of the frontal lobe in connection with subcortical structures may underlie the “affective” symptoms observed among individuals with depression. Positron emission tomography (PET) studies have also shown processing deficits in the frontal lobes and have related these to problems with decision making (Drevets et al., 1997). Furthermore, PET studies have revealed decreased 5-HT2 receptor density in the frontal cortex in those with remitted depression (Larisch et al., 2001). Decreased 5-HT2 receptor density may serve as an indicator of MDD susceptibility, as these receptors maintain regulation of mood, sleep, aggression, sexuality, and appetite.

Genetics of Depression Genetic studies have sought to identify the genes that are associated with increased risk for the development MDD. Studies often focus on genes that may impact monoamine neurotransmitters. Polymorphisms in the serotonin transporter promoter region (5-HTTLPR) have received a great deal of scrutiny and have been linked to bipolar disorder, depressive traits, and suicidal behavior, but they have yet to reveal a direct association with MDD (Levinson, 2006).

Cognitive Impairment in Depression Although there is controversy surrounding the subject, research suggests that at least a subset of persons with depression may have mild to moderate NP impairment (King

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& Caine, 1996; Landro, Stiles, & Sletvold, 2001; Zakzanis, Leach, & Kaplan, 1998). Cognitive impairment appears to be more prevalent among depressed individuals who are older, have a poorer response to antidepressant medications, suffer recurrent episodes, and have a younger age of onset (Jaeger, Berns, Uzelac, & Davis-­Conway, 2006). The diagnosis of “major depressive disorder,” as presented in the DSM-IV-TR, is based on a list of heterogeneous symptoms (American Psychiatric Association, 2000). Some of these symptoms are cognitive in nature. Specifically, one of the criteria, “reduced ability to think or concentrate,” may be related to attentional deficits. Decreased attention and concentration can include negative thought rumination, which has been shown to impact social problem solving (Donaldson & Lam, 2004). Another of the criterion, “psychomotor agitation or retardation,” is often manifested as reduced eye movements, constricted posture, shortened speaking time, slowed speech responses, poor articulation, increased restlessness at night, and continuous hand-to-head touching (Sobin & Sackeim, 1997). In NP terms these deficits may be manifested through decreased psychomotor speed and impaired speed of information processing. Such disruptions may manifest themselves differently in terms of features, severity, and permanence (King & Caine, 1996). In the past, the term “pseudodementia” was used to describe primarily older patients with NP difficulties caused by a psychiatric illness rather than by a neurodegenerative disease. It is important to distinguish between the manifestations of cognitive difficulties in patients with these very different diagnoses. Mood-­induced cognitive difficulties typically develop over a fairly short period of time and distress the patient, unlike similar changes due to a neurodegenerative disease (Arnold, 2005). Patients with dementia rarely show improvements in cognitive tests of memory, whereas many patients with depression improve their memory performance over time (Jenike, 1988). Underlying global abilities that may be lost in dementia, such as language and learning skills, are still intact in depressed individuals (Arnold, 2005). We believe that “pseudodementia” inaccurately describes the syndrome, downplaying the substantive cognitive problems experienced by individuals with depression. In other words, NP impairment should be viewed as a result of disruption in brain functioning, not just a product of depressed mood. In terms of the severity of cognitive impairment, a well-­designed study examining NP impairment in individuals with schizophrenia, nonpsychotic depression, and healthy controls showed that individuals with schizophrenia have the most significant cognitive impairment; however, individuals with nonpsychotic depression showed NP impairment in two of seven cognitive domains, as compared to zero domains among healthy comparison participants (Rund et al., 2006). Although severity of depressive symptoms did not directly correlate with NP performance, this study found that 24% of the patients with recurrent MDD had moderate cognitive impairment, and 4% of patients had severe impairment. Moderate impairment was defined as performing 1.5 standard deviations below the healthy control mean in at least two of seven NP domains, and severe impairment was defined as impairment in at least five domains. For comparison purposes, 45% of individuals with a diagnosis of schizophrenia had moderate impairment and 17% had severe impairment. For the depressed group, impairments were most apparent in the domains of working memory and reaction time.

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The Impact of Severity, Clinical State, and Remission of Depression on Cognition The evidence is somewhat mixed with regard to whether the overall extent of depressive symptoms relates to cognitive ability. The study described above reported no correlation between severity of depressive symptoms and NP performance, whereas other studies have shown that severity of depression and number of affective episodes can be associated with worse NP ability (Grant, Thase, & Sweeney, 2001; Kessing, 1998). In patients with more severe symptoms, performance on motor tasks has previously been shown to be worse, but degree of memory impairment has been shown to be negatively correlated (Jenike, 1988). However, other researchers suggests that the probability of finding memory deficits may correlate with symptomatic severity. Obviously the relationship between depression and cognitive ability is complex. There is currently no consensus as to persistence, severity, or pattern of cognitive deficits in depression (King & Caine, 1996). The effect of an individual’s current clinical state on depression is also unclear. Some studies find that level of depressive symptoms relates to cognitive functioning (Grant et al., 2001). The evidence is stronger that some persons with depression who have cognitive impairments will continue to have such problems after they have returned to a euthymic state or their symptoms have remitted (Silverstein, Harrow, & Bryson, 1994). Remitted MDD individuals in euthymic states still show significant attention and executive functioning impairments as compared to healthy controls (Clark, Sarna, & Goodwin, 2005; Paelecke-­Habermann, Pohl, & Leplow, 2005). Such persistence of cognitive difficulties may suggest an underlying neural dysregulation among some persons, which influences the presentation of cognitive symptoms associated with MDD (King & Caine, 1996). The cause of NP impairment in depressed individuals does not appear to be simply a result of low mood; instead impairment may be a manifestation of many neurobiological traits. Another important issue to clarify is whether or not subjective cognitive complaints correlate to objective findings from NP testing. Skeptics suggest that cognitive deficits in individuals with depression is a result of loss of motivation or attention, not a reproducible neural dysfunction. In one study it was found that self-­reported cognitive deficits only predicted impairment in memory retention and concentration, not psychomotor speed, initial learning, or executive dysfunction (Naismith, Longley, Scott, & Hickie, 2007). The validity of self-­ratings must be taken into consideration when exploring NP deficits.

The Effect of Treatment on Cognition What remains particularly unclear is whether pharmacological treatments for depression contribute to the cognitive deficits in this disorder; however, it would be a misconception to think that cognitive problems among individuals with depression are an epiphenomenon caused by the treatment of the disorder. A recent review showed that, at least in specific executive functioning domains (e.g., set shifting), cognitive deficits do not necessarily improve with resolution of clinical symptoms. Deficits on executive functioning tasks are consistent with damage to dorsal and ventral portions of the prefrontal cortex (Austin, Mitchell, & Goodwin, 2001). These persisting cog-

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nitive deficits may have considerable implications for everyday functioning in persons treated for depression.

Cognitive Domains Commonly Impaired in Individuals with Depression Although individuals with depression may show cognitive impairments in a range of domains, we have chosen to focus on impairments in the areas of executive functioning, learning and memory, motor skills, and psychomotor speed because they appear to be the most common (Tavares, Drevets, & Sahakian, 2003). The majority of NP deficits observed in those with depression are consistent with the frontosubcortical pathology described in the previous neurobiology/neuroanatomy section. Other cognitive abilities shown to be impaired in individuals with depression, such as an abnormal response to negative feedback and an affective processing bias, are reviewed elsewhere (Chamberlain & Sahakian, 2006; Tavares et al., 2003). Executive functioning deficits are some of the most frequently identified and debilitating impairments among those with depression (Clark et al., 2005; Veiel, 1997). There is evidence to suggest that attention and executive functioning deficits in depression are trait-based and not a direct result of the depressive symptoms. This finding is supported by the earlier cited fact that remitted MDD individuals in euthymic states still show significant attention and executive functioning impairments, as compared to healthy controls without evidence of lifetime MDD (Clark et al., 2005; Paelecke-­Habermann et al., 2005; Veiel, 1997). This does not mean that impairments in these domains are not related to clinical state. For example, in a cohort of individuals with mild to moderate levels of depression, executive functioning was the only domain of impaired functioning (attention, memory, and motor skills were normal), and certain executive functioning deficits were related to severity of depressive symptoms (Grant et al., 2001). Executive functioning impairments are more visible when the severity of the depression increases (Boone et al., 1995). Impairments in executive functioning may be particularly relevant to everyday functioning. Preliminary evidence of the effect of executive dysfunction has been shown in several areas of daily functioning, such as difficulties with planning and executing goal-­directed activities. For instance, depression can lead to impairments in vocational and social abilities. Additional details regarding the effect of depression on executive functioning ability are reviewed elsewhere (DeBattista, 2005). Learning and memory problems in some individuals with depression have been clearly identified (Goodwin, 1997). These cognitive difficulties seem to be found on both verbal and visual learning and memory tasks (Austin et al., 2001). One interesting study showed that individuals with depression (in either a current episode or with evidence of a past episode) had difficulties with delayed recall, but did not have difficulties with habit-­learning tasks, suggesting dysfunction of medial temporal systems rather than striatal systems (MacQueen, Galway, Hay, Young, & Joffe, 2002). Deficits were shown to be related to number of previous episodes, but independent of current mood state. There is research, however, that has failed to find significant learning and memory problems in euthymic patients with a diagnosis of unipolar depression (Clark et al., 2005). Some have argued that learning and memory problems may largely be a state phenomenon, wherein individuals with MDD who are currently euthymic or remitted do not show these difficulties (Clark et al., 2005;

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Sheline, Sanghavi, Mintun, & Gado, 1999). In short, the evidence appears to be somewhat mixed with regard to the root cause of learning and memory difficulties in persons with depression. Variation in the findings of pertinent studies may be due to methodological problems and the heterogeneity of the “syndrome” of MDD. Psychomotor slowing is another common impairment in persons with depression, and, again, this symptom can aid in the diagnosis of a major depressive episode. This slowing can negatively influence performance on NP tests that are sensitive to generalized slowing, such as computerized reaction time measures (Elliott et al., 1996).

Cognitive Problems in Older Individuals with Depression Some of the most consistent evidence linking depression to NP dysfunction comes from studies of older people, in whom disturbances in executive function can be prominent (Alexopoulos, Kiosses, Klimstra, Kalayam, & Bruce, 2002; Alexopoulos, Kiosses, Murphy, & Heo, 2004; Alexopoulos et al., 2000). Given that depression is prevalent in older age, and the fact that cognitive problems can increase among older adults, this is a particularly important concern (Steffens et al., 2006). Some investigators have suggested that impaired cognition may be limited to patients experiencing somatic symptoms of depression. For example, depressed patients with primarily vegetative (e.g., sleep, appetite) symptoms preformed significantly worse on NP tests of nonverbal intelligence, visual memory, and abstract problem solving, as compared to depressed patients with primarily psychological (e.g., mood) symptoms (Palmer et al., 1996). Other studies have suggested that cognitive problems are not necessarily more common with increasing age, rather that there may be “premature” aging in certain cognitive domains (e.g., nonverbal memory, word generation, and certain frontal lobe skills) (Boone et al., 1994). Given that difficulties with daily functioning can be more common among older adults, the presence of depression may exacerbate these difficulties. The particular impairments in daily functioning are further discussed in the section on depression and everyday functioning below.

Cognitive Impairment in Psychotic versus Nonpsychotic Depression Data indicate that cognitive impairment tends to be worse in those with psychotic as compared to nonpsychotic depression, but the exact pattern of these differences remains somewhat unclear (Fleming, Blasey, & Schatzberg, 2004; Jeste et al., 1996). Some have found that individuals with psychotic depression have more diffuse cognitive impairment as compared to those with nonpsychotic depression (Basso & Bornstein, 1999). The implications of psychotic versus nonpsychotic depression for daily functioning have not been explored; however, one can hypothesize that the additional cognitive impairment in persons with psychotic depression may translate into additional functional difficulties.

Mood-­Congruent Cognitive Processing One interesting phenomenon among individuals with depression is that they tend to show preferential processing for emotional stimuli with a negative tone (Ellis & Moore, 2001). For example, depressed patients are able to more easily recall a story

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with negative emotional content, and they show an above-­average ability to recall negative emotional events from the past (Blaney, 1986; Brittlebank, Scott, Williams, & Ferrier, 1993; Williams & Scott, 1988).

Depression and Everyday Functioning Depression and Performance of Activities of Daily Living and Instrumental Activities of Daily Living Individuals suffering with depression show a range of ability to independently carry out basic self-care such as personal hygiene (activities of daily living [ADLs]) and complete more complex tasks such as making and keeping appointments (instrumental activities of daily living [IADLs]). The presence of depressive symptoms is associated with a decline in performance of ADLs, particularly among older individuals. In an older population, 30–60% of individuals who have been hospitalized for a medical condition experience a drop in their ability to perform ADLs, including bathing, dressing, toileting, transferring, and eating (Hoogerduijn, Schuurmans, Duijnstee, de Rooij, & Grypdonck, 2007). Perhaps the largest study of the effect of depressive symptoms on ADLs and IADLs evaluated 572 recently hospitalized older individuals (Covinsky, Fortinsky, Palmer, Kresevic, & Landefeld, 1997). These authors found that persons with more depressive symptoms were significantly more likely to be dependent on others to help them perform ADLs as compared to individuals with no or few depressive symptoms. This finding was also true for IADLs, which include taking medicine, handling finances, managing transportation, and using the telephone. Other studies have confirmed that depressive symptoms are strongly associated with poor functional performance, and depression scores for older individuals were significantly higher for those who reported experiencing ADL decline (Covinsky et al., 1999; Lenze et al., 2005; Wakefield & Holman, 2007; Wu et al., 2000). Among community-based samples of older adults, NP impairment and depression appear to be two of the strongest predictors of daily functioning problems, even when controlling for baseline cognitive function, alcohol consumption, and chronic health conditions (Stuck et al., 1999). Among persons with a primary major depressive episode and severe depressive symptoms (e.g., Beck Depression Inventory M = 34.3; SD = 11.0), cognitive impairment was strongly associated with impairment in IADLs, such as medication taking and finance handling, whereas level of depressive symptomatology and age were more strongly associated with impairments in basic ADLs (McCall & Dunn, 2003). Severity of depression and age were also associated with patients’ satisfaction in role functioning and relationships.

Depression and Psychosocial Functioning Like cognitive problems, psychosocial dysfunction is both a part of the diagnostic criteria for MDD as well as a consequence of the disorder. Specifically, depression is associated with declines in job status, income, and sexual activities; difficulties with marriage; and problems in familial relationships and friendships. Patients with more severe depressive symptomalogy exhibit higher levels of psychosocial dysfunction

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(Coryell et al., 1993; Judd et al., 2000). Residual and pervasive depressive symptoms following treatment may lead to continued psychosocial dysfunction, suggesting that functional recovery lags considerably behind clinical recovery (Kennedy, Foy, Sherazi, McDonough, & McKeon, 2007). Additionally, problems in planning, working memory, and attention may be linked to permanent changes in the brain function of individuals with depression. These deficits in cognitive functioning may directly affect social functioning (Kennedy et al., 2007). Others may argue that low mood has less of a direct effect on neurocognitive integrity and a more consequential effect on psychosocial functioning, as difficulties in this domain may be more visibly troublesome.

Depression and Medication Adherence/Management Although antidepressant medications are effective in reducing symptoms in many individuals with depression, this efficacy does not in itself ensure adherence to prescribed medications. In fact, randomized controlled trials of treatments for depression show that 20–40% of patients stop their treatments prior to completing a 6-month trial (Frank & Judge, 2001). In less stringent trials, the adherence rates appear to be approximately 50% (Demyttenaere, 2003). A recent review of medication adherence patterns among individuals with depression suggests that nonadherence may be largely driven by negative attitudes toward medication and depression as well as fear of medication dependence (Hansen & Kessing, 2007). Other reasons for nonadherence to medications include side effects and illness denial, although it has been suggested that beliefs about the efficacy of antidepressant medications may outweigh side effect problems (Byrne, Regan, & Livingston, 2006; Hansen & Kessing, 2007). Cognitive impairment may be another predictor of nonadherence (e.g., if a person has difficulty remembering to take his or her medication, he or she is less likely to be compliant). This is especially true of elderly patients with memory problems (Ayalon, Arean, & Alvidrez, 2005). Several psychosocial treatments have been designed to help individuals improve their adherence abilities. Briefly, collaborative interventions that involve the patient, significant others, as well as the physician have proven to be the most helpful for improving adherence in this group (Vergouwen, Bakker, Katon, Verheij, & Koerselman, 2003). Even relatively simple adherence interventions, such as use of external reminders or construction of a positive attitude toward medication, appear to improve medication adherence (Patel & David, 2005). Cultural considerations must be taken into account when addressing the issue of adherence as some ethnic/racial groups may have different preferences with regard to the treatment of depression. For example, Hispanic individuals may have a preference toward psychotherapy or combination therapy as opposed to pharmacotherapy alone (Lewis-­Fernandez, Das, Alfonso, Weissman, & Olfson, 2005).

Depression and Vocational Functioning In addition to having a direct impact on simple daily functions, depression can negatively affect the ability to seek out and maintain employment. One study estimated the cost of time lost at work due to depression to be $31 billion (Stewart, Ricci,

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Chee, Hahn, & Morganstein, 2003). Although depression may impact the likelihood of garnering employment, many individuals with depression are employed. Within the working population, depression prevalence rates have been estimated at approximately 2–4% (Dewa & Lin, 2000; Kessler & Frank, 1997; Kouzis & Eaton, 1997). It is likely that this prevalence underestimates the actual problem, given the documented pattern of individuals with depression to report physical problems (e.g., back pain) instead of emotional or psychological problems. Failure to report depression in the workplace may be driven by both associated stigma as well as compensation policies (i.e., employees may be reimbursed for physical problems but not necessarily psychological or emotional problems). Depression has had a definitive, negative effect on short-term disability claims (i.e., loss of work for 1–30 days) among the employed. In the early 1990s the proportion of mental-­health-related short-term disability claims doubled (Health Insurance of America, 1995). Furthermore, depression accounted for over half of the claims that were filed for reasons of mental health or nervous disorders. Several studies show the widespread impact of depression on days of work lost due to short-term disability. One study showed that the likelihood of taking short-term disability among persons with depression was 37–48% as compared to 17–21% among those without depression (Dewa, Goering, Lin, & Paterson, 2002). Another study suggested that approximately 2.5% of short-term disability claims were depression­related when examining an administrative data set of approximately 63,000 Canadian employees (Dewa et al., 2002). Those who filed depression-­related claims tended to be women between the ages of 36 and 55. This study also showed that among an already working population depression was more likely to cause difficulties for employees than other mental illness problems, responsible for a greater duration away from employment, and more likely to recur. Another detailed study showed that depression can be as debilitating, in terms of days lost from work, as serious medical illnesses. Specifically, in a 12-year study of employees (two-­thirds of whom were women) at a major national bank, depression accounted for 65% of total short-term disability days with an average of 44 days of work lost. For comparison purposes, employees tended to take an average of 42 days for heart disease and 39 days for lower back pain. Severity and recurrence of depression were the strongest predictors of level of functional and work disability (Rytsala et al., 2005). Some effective interventions, such as the Quality Enhancement by Strategic Teaming (QuEST), which includes enhanced symptom monitoring and subsequent medication adjustments, have been applied with successful outcomes. Such enhancements in treatment appear to both increase the likelihood of returning to work and reduce the number of workplace conflicts among those returning to work following a depressive episode (Smith et al., 2002).

Depression and Driving As has been shown in other functional domains, it is difficult to disentangle the impact of depressive symptoms and treatment for depressive symptoms on driving ability. Epidemiological studies appear to suggest that individuals who are taking sedating antidepressant medications may be at greater risk for traffic accidents (Leveille et al., 1994; Ray, Fought, & Decker, 1992). To our knowledge, only two studies have examined actual driving performance among patients with depression, and

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the findings have been somewhat inconsistent. One showed that patients taking an SSRI or a selective norepinephrine reuptake inhibitor (SNRI; inhibits the reuptake of both norepinephrine and serotonin) showed poorer driving performance on an on-road driving test, as compared to matched healthy controls (Wingen, Ramaekers, & Schmitt, 2006). Driving performance appeared to be related to continuing depressive symptoms, especially suboptimal arousal. An earlier study showed that driving performance was not related to improvement or decline on the Hamilton Depression Rating Scale (Ramaekers, Ansseau, Muntjewerff, Sweens, & O’Hanlon, 1997). We are unaware of any studies examining driving among individuals with depression who are not currently receiving treatment.

Depression and Quality of Life Undoubtedly, depression can negatively impact a person’s quality of life (Pyne, Smith, et al., 2003; Wells et al., 1989). The difficulty with studies of depression and quality of life is that some investigators feel that poor quality of life is simply part of the depressive illness and is not distinct from depressive symptomatology. When various predictors of quality of life are examined, severity of depressive symptoms has been shown to be most strongly associated with subjective quality of life (Corrigan & Buican, 1995). Using the self-­administered and interviewer-­administered Quality of Well-Being Scale (QWB), researchers have shown that level of depression is significantly correlated with QWB scores such that greater levels of depression are related to poorer quality of life (Pyne, Sieber, et al., 2003). Given the strong correlation between depressive symptoms and quality of life, one recent review called for the targeting of depressive symptoms in improving overall quality of life (Hansson, 2006).

Depression in the Context of Other Neurological/Psychiatric Conditions Thus far we have discussed the impact of depression on cognition and everyday functioning in isolation, but it is well known that depression and/or depressive symptoms are a common consequence of many medical, neurological, and psychiatric conditions. To cover all aspects of the effects of depression on daily functioning in the context of all other medical conditions would not be feasible in this chapter; however, it is worth providing a couple of examples of how depressive symptoms can influence everyday functioning in the presence of comorbid syndromes. Clinically significant depressive symptoms are common among individuals with HIV infection (Ciesla & Roberts, 2001). A large study of the everyday functioning abilities of individuals with HIV infection found that depressive symptoms, as measured by the Beck Depression Inventory, were a significant predictor of employment (Heaton et al., 2004). Levels of depressive symptoms and levels of functional impairment, as measured by laboratory-based IADL tests, were also correlated with patients’ complaints of cognitive difficulties. Depressive symptoms uniquely contributed to participants’ subjective complaints, as the symptoms did not strongly relate to levels of functional impairment. Finally, higher levels of NP impairment, functional impairment, and depressive symptoms contributed to greater dependence in the performance of daily activities such as cooking, shopping, laundry, home repair, and comprehension of reading material.

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Depressive symptoms can also play a role in other psychiatric disorders. For instance, in a recent study of the functional abilities of individuals with schizophrenia, it was shown that depressive symptoms were significant predictors of real-world outcomes involving interpersonal skills and work skills independent of NP problems or other psychiatric symptoms (Bowie, Reichenberg, Patterson, Heaton, & Harvey, 2006). In conclusion, it may be important to assess for the influence of depressive symptoms on everyday functioning regardless of the clinical population.

Summary Future Directions for Research There is clearly a general gap in the literature with regard to everyday functioning among persons with depression. There is a sufficient body of research focusing on the impact of depression on vocational functioning and as a predictor of short-term disability. However, the interplay between cognitive functioning associated with depression and everyday skills has not yet been studied directly. Research into the interplay among depressive symptoms, cognitive abilities, functional capacity, and real-world functional outcomes would undoubtedly expand our understanding of depression and how this condition should be treated. At this point, we lack the ability to make statements about improvements in daily functioning with the resolution of clinical symptoms alone.

Conclusions The multifaceted syndrome of depression is a common problem among individuals worldwide. The effects of this syndrome extend beyond its clinical symptoms (e.g., low mood, loss of interests in previously pleasurable activities) to problems with cognition and everyday functioning (see Figure 17.1). Cognitive problems are most prominent in the areas of executive functions, learning and memory, and psychomotor slowing. One of the major implications for everyday functioning in individuals with depression is decreased ability to function in an employment setting, and depression has been shown to be a major contributor to short-term disability claims among those who are employed. Efficacious treatments for depression are available (both pharmacological and psychosocial) and may help to resolve clinical symptoms, cognitive problems, and difficulties in everyday functioning, although it is difficult to tease apart the various contributors to cognitive impairment and everyday functioning. In sum, clinicians should be particularly sensitive to the high base rates of depression (regardless of clinical population) and the damaging effect that this illness can have on daily functioning.

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Chapter 1 8

Cognition and Daily Functioning in Schizophrenia Michael F. Green

Features of Schizophrenia Schizophrenia is known for its dramatic clinical features, including psychotic symptoms (e.g., hallucinations and delusions), negative symptoms (e.g., flattened affect, reduced motivation, reduced speech), and disorganized symptoms (e.g., vague or tangential speech, odd behaviors) (American Psychiatric Association, 1994). Less obvious to many people is that schizophrenia is also characterized by prominent cognitive impairments (Green, 2001). Although schizophrenia has long been seen as a disorder of the brain, and one characterized by perceptual aberrations, it has not traditionally been viewed as a neurocognitive disorder. In this regard it is different from many of the other neurological conditions covered in this volume. That view of schizophrenia is changing, and the cognitive performance impairments in schizophrenia now are a recognized part of the illness. The cognitive deficits associated with schizophrenia are fairly broad and encompass a wide range of domains. This broad pattern of pattern of impairment, along with the fact that some patients perform in the normal range on certain tests, are among the reasons that it has been difficult to identify particular neural circuits that are specific to schizophrenia. Among the many domains affected in schizophrenia, some have been selected as particularly important for clinical trials. Based on a careful literature review and consensus meetings sponsored by the National Institute of Mental Health (NIMH), the following separable cognitive domains were selected as important to assess in treatment studies of cognition in schizophrenia: speed of processing, attention/vigilance, working memory, verbal learning, visual learning, reasoning and problem solving, and social cognition (Nuechterlein et al., 2004). Although there is considerable between-­subject variability in the pattern of deficits, there is also a modal neurocognitive profile that is characterized by larger deficits (in the range of 1.5 SDs or more) in verbal learning and vigilance, and lesser impairments in visual organization abilities and vocabulary (Heinrichs & Zakzanis, 1998).

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Cognitive impairments are relatively common in schizophrenia. It has been estimated that 90% of patients have clinically meaningful deficits in at least one cognitive domain and that 75% have deficits in at least two (Palmer et al., 1997). Others have suggested that even these relatively high rates of impairment may be underestimates and that almost all patients with schizophrenia may perform at a level below what would be expected in the absence of illness. Such estimates are based on the cognitive performance of patients compared to their unaffected monozygotic twins (Goldberg et al., 1990) or to estimates of expected levels based on premorbid functioning (Kremen, Seidman, Faraone, Toomey, & Tsuang, 2000). Cognitive impairments in schizophrenia have been noted and clearly described for well over a century and so cannot be considered a new discovery (Bleuler, 1950; Kraepelin, 1971). Because the impairments were appreciated so long ago, the recent surge in interest is more of a rediscovery than a discovery. At any rate, much more is known now about the nature of the deficits. The impairments are now clearly viewed as “core” features of the illness and not as secondary to the illness. The term “core” means that the impairments do not result merely from the presence of psychotic symptoms (e.g., distractibility due to hallucinations) or due to the psychopharmacological treatments (e.g., sedation due to antipsychotic medications). Evidence for the central nature of these deficits in schizophrenia comes from several lines of research. A brief listing of the lines of evidence is presented below; detailed reviews of this topic can be found elsewhere (Braff, 1993; Gold, 2004; Gold & Green, 2004; Green, 2007; Nuechterlein, Dawson, & Green, 1994). 1.  Many patients demonstrate cognitive or intellectual impairments before the onset of psychotic symptoms and other clinical features of the disorder; hence the cognitive impairments predate and show a different time course than clinical features of illness. 2.  Cognitive impairment (at attenuated levels) can be detected in first-­degree relatives of patients with schizophrenia who are not psychiatrically ill. The presence of deficits in unaffected relatives suggests that some of the impairments reflect predisposition to schizophrenia, as opposed to the presence of the illness. For this reason, cognitive impairment is being used as an endophenotype in genetic studies of schizophrenia. 3.  The magnitude of the cognitive impairment is relatively stable across clinical state, with the level of impairment on some cognitive measures being quite similar when patients are in, or out, of a psychotic episode. Hence, the impairments can occur in the absence of clinical symptoms of schizophrenia. 4.  Cross-­sectional correlations between cognitive performance and ratings of psychotic symptom severity are typically very small. The low correlations are especially true for psychotic symptoms. Correlations with negative and disorganized symptoms are sometimes larger, but still relatively modest. 5.  The effects of antipsychotic medications are much larger on psychotic symptoms of schizophrenia than they are on cognition. There may be greater cognitive benefits for second-­generation medications (i.e., atypicals) compared to first-­generation medications, but even so, this discrepancy of cognitive and clinical effects is true for

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both types of drugs. It suggests that the antipsychotic medications act on different neural systems from those that underlie the cognitive impairments. Based on these converging lines of evidence, it can be concluded that cognitive impairment is a central feature of schizophrenia and that it is very prevalent. Although this conclusion seems obvious now, it reflects a recent shift in focus: away from the typical psychotic and negative symptoms that are part of the diagnostic criteria to the less dramatic, but more enduring, cognitive deficits. The focus on cognitive impairment is also consistent with the vast neuroimaging literature in schizophrenia showing a range of structural (Lawrie, Johnstone, & Weinberger, 2004; Narr et al., 2004) and functional (Glahn et al., 2005; Holmes et al., 2005) abnormalities in the disorder.

Disability and Outcome in Schizophrenia Schizophrenia is a highly disabling illness. The illness impacts essentially every aspect of daily functioning, including social networks, closeness to family members, school and vocational success, performance of activities of daily living (ADLs), and degree of independent living. When considering all causes of disability, schizophrenia ranks among the top five causes of disability for young adults in developed countries (Murray & Lopez, 1996). This high ranking is true for both men and women, even though schizophrenia tends to have earlier onset and be somewhat more severe for men. Functional outcome in schizophrenia is typically assessed through semistructured interviews or surveys in which the participant describes his or her participation in various daily activities (Birchwood, Smith, Cochran, Wetton, & Copestake, 1990; Weissman & Paykel, 1974). Self-­report ratings of functioning can be supplemented with ratings from caregivers, but typically they are not. It is rare for outcome studies in schizophrenia to use observations of behaviors in the community, so questions are sometimes raised about the validity of self-­report measures (Bellack et al., 2007). Nonetheless, self-­report ratings are generally considered to be acceptable measures of functioning for patients who are clinically stable. The relatively poor functional outcome in schizophrenia has changed little over the last century, even with the introduction of efficacious antipsychotic medications in the 1950s (Hegarty, Baldessarini, Tohen, Waternaux, & Oepen, 1994). This reality creates a confusing situation in which antipsychotic medications (both first- and second-­generation medications) are highly effective in reducing psychotic symptoms, but patient outcomes have not improved. It is hard to understand why, if our drug treatments are so good, the outcomes are so bad. One way to resolve the situation is to differentiate the types of outcome in schizophrenia. There are at least three distinctly different types of outcome in schizophrenia: clinical, subjective, and functional (Brekke, Levin, Wolkon, Sobel, & Slade, 1993; Brekke & Long, 2000). The clinical outcome includes levels of persisting psychotic and negative symptoms; subjective outcome refers to how good the patients feel about themselves and how satisfied they are with their lives. Neither of these types of outcome has a strong relation-

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ship to functional outcome, which includes social functioning, vocational success, and degree of independent living. Making the distinction among different types of outcomes helps clarify the picture. Antipsychotic drugs are clearly effective in reducing symptoms in the majority of patients, and this effect is related to clinical outcome. However, antipsychotic medications have minimal effects on other features of illness, such as cognitive impairments. As we will see in the next section, level of cognitive functioning is related to degree of daily functioning in schizophrenia. Hence, the features of illness that are related to functional outcome (e.g., cognitive impairments) are not impacted by drugs; instead the drugs improve aspects of illness such as psychotic symptoms that have comparatively less impact on daily functioning. The result of this mismatch is a major public health concern: Most patients with schizophrenia do not successfully reenter the community (defined by social or work achievements) after onset of illness (Hegarty et al., 1994; Helgason, 1990; Wiersma et al., 2000). The treatment of schizophrenia can be viewed in terms of two phases: short term and long term. When someone experiences a psychotic episode, the first challenge is to reduce symptoms and to clinically stabilize the individual. The second phase occurs after the individual and the situation are stable and he or she is seeking a return to work, school, or family. The first phase tends to be managed successfully with medications and treatment teams; the second phase tends to end in disappointing outcomes.

Cognitive Impairment and Daily Activities in Schizophrenia There is a rather large literature on the relation between cognitive impairment and functional outcome in schizophrenia; I would estimate that around 100 data-based papers have been published on this topic. Papers started to be published in the early 1990s and have continued until the present. Many of the published studies have been included in three literature reviews from our group (Green, 1996; Green, Kern, Braff, & Mintz, 2000; Green, Kern, & Heaton, 2004). The three reviews (including one meta-­analysis) concluded that cognitive deficits show reliable relationships to functional outcomes in schizophrenia. Many of the studies also included patients with schizoaffective disorder, but it is generally referred to as a literature on schizophrenia. Among the studies, functional outcomes have included types of community functioning (social outcome, vocational success, and independent living), as well as the degree of success in acquiring skills in psychosocial rehabilitation programs. Participation in psychosocial rehabilitation groups can be considered a daily activity for many patients with schizophrenia, so it is reasonable to consider success in these programs as a form of functional outcome. Across studies, the consistency of relationships is striking, and this overall conclusion is no longer a subject of debate. The strengths of the associations are typically in the medium range (e.g., r = .3) when separate cognitive domains are considered. The relationships can be much stronger (r = .5 or greater) when multiple cognitive domains are combined into composite scores (Green et al., 2000). At this point, the simple conclusion that cognitive performance is related to daily functioning in schizophrenia is clear and warranted. However, several followup questions deserve careful attention.

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•• Do the relationships hold for prospective, as well as cross-­sectional, associations? One of the reviews was devoted to prospective studies in which baseline cognition was correlated with community functioning (defined in terms of work status, social functioning, or degree of independent living) at a minimum 6-month follow (Green, Kern, et al., 2004). This review included 18 longitudinal studies that all appeared subsequent to the earlier review that was published in 2000. Based on the survey of these studies, it appears that cognitive impairment at a baseline assessment is a reasonable predictor of later community functioning. In fact, several of the studies found good associations with outcome 2–4 years after baseline assessment (Dickerson, Boronow, Ringel, & Parente, 1999; Friedman et al., 2002; Gold, Goldberg, McNary, Dixon, & Lehman, 2002; Robinson, Woerner, McMeniman, Mendelowitz, & Bilder, 2004; Stirling et al., 2003). Several studies in the review examined baseline prediction of changes in functional outcome, instead of only functional status at follow-up (Friedman et al., 2002; Smith, Hull, Huppert, & Silverstein, 2002; Woonings, Appelo, Kluiter, Slooff, & van den Bosch, 2002). Such findings of baseline cognition predicting change in functional outcome are important because they indicate that cognitive status has value for predicting how well people will benefit from interventions that are designed to improve community functioning (e.g., skills training programs). It is also possible to examine change in cognition over time, as opposed to change in functioning, and two of the studies found correlations between cognitive change and functioning (Friedman et al., 2002; Stirling et al., 2003). It should be noted that these studies examined cognitive decline, not improvement. In the absence of a potent cognitive enhancer, it has been hard to study correlates of cognitive improvement. •• Are some cognitive domains more strongly related to outcome than others? The findings in this regard have been mixed, with some studies suggesting that verbal learning (Green et al., 2000) or speed of processing (Gold et al., 2002) may be particularly important for functional outcome. However, looking across studies at this time, it is not obvious that one domain is particularly important to outcome compared to others. Instead, most or all of the cognitive domains appear to be related to functioning, at least when findings are averaged across subjects (Evans et al., 2003; Green et al., 2000; Velligan, Bow-­Thomas, Mahurin, Miller, & Halgunseth, 2000). It is entirely possible that specific domains are more important for certain individuals. However, due to the large individual differences in which cognitive domains are most impaired, these individual patterns may wash out when the data are analyzed by groups. •• Are specific cognitive domains related to specific aspects of functioning? At this point it is difficult to draw connections between specific cognitive domains and particular aspects of outcome (e.g., work vs. social outcome, skill acquisition vs. independent living). Some support for specific differential relationships has come from a recent study that attempted to identify latent cognitive constructs and their relationships to outcome (Jaeger et al., 2006). Differential associations were found between cognitive domains and work outcome versus residential outcome. For example, working memory was associated with work/education outcome, but the domains of divergent thinking, cognitive flexibility, and speed were associated with residential outcome.

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Some investigators have examined the cognitive correlates of specific types of outcome, such as driving ability (Palmer et al., 2002; St. Germain, Kurtz, Pearlson, & Astur, 2005). Considerations of the predictors and correlates of work performance are useful, especially because work outcome (whether someone has a job, hour many hours a week, how long he or she has maintained the job) is a rather concrete, objective, and verifiable outcome. As expected, both the likelihood of having a job and the length of job tenure are consistently related to cognitive abilities (Bell & Bryson, 2001; Bryson & Bell, 2003; Gold et al., 2002; Rosenheck et al., 2006). One type of outcome that might be expected to be related to cognitive functioning, namely, medication adherence, is not consistently related. For example, a largescale multisite 2-year follow-up study of patients with first-­episode schizophrenia did not find level of cognitive functioning to be a predictor of medication adherence (Perkins et al., 2006). Instead, beliefs about the need for medication and the efficacy of the medications predicted adherence. Similarly, a review of the literature concluded that there is little support for cognitive status as a predictor of medication adherence (Lacro, Dunn, Dolder, Leckband, & Jeste, 2002). One might expect that lack of medication adherence would be related to memory failure, and in fact a study did find a correlation between a simulated assessment of medication adherence and the Mini-­Mental State Examination (MMSE; Patterson et al., 2002). However, in schizophrenia it appears that factors related to insight, as well as belief in the need and value of treatment, may be more important than level of cognitive functioning. Even when specific relationships to a type of outcome are uncovered, they may change over time. For example, one study reported that vigilance is more important than verbal memory in explaining work performance during a structured 26-week vocational program (12 vs. 4% variance explained)—but only for the first half of the program (Bryson & Bell, 2003). The pattern was reversed for the second half of the program, in which verbal memory was a stronger predictor than vigilance (11 vs. 6%). In this case, familiarity with the tasks appeared to change the type of cognitive demands. Given this level of complexity, it is safe to say that it will take more time and more studies with differentiated assessments to form conclusions about highly specific relationships to outcome. •• Are the relationships present for other major psychiatric disorders? It is clear that these patterns of relationships are not diagnostically specific to schizophrenia, but also apply to other psychiatric disorders. Chapter 17 in this volume examines cognition and functioning in depression, so here we briefly consider the data for bipolar disorder. Compared to the large number of studies on this topic in schizophrenia, the literature on bipolar disorder is paltry. However, a few findings suggest that similar relationships between cognition and functioning are present for bipolar disorder and that the strengths of these associations are comparable to those seen in schizophrenia (Dickerson et al., 2004; Martínez-Arán et al., 2004). Bipolar disorder is associated with cognitive impairment even when patients are in a euthymic state (Altshuler et al., 2004; van Gorp, Altshuler, Theberge, Wilkins, & Dixon, 1998), so many of the same concerns that apply to schizophrenia, about achieving adequate community functioning after acute treatment, also apply to bipolar disorder. It is possible that the cognitive domains that are predictors of outcome for bipolar disorder will be different from those of schizophrenia, perhaps because of differ-

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ences in the typical daily tasks for individuals with each disorder. Along these lines, it was suggested that verbal memory may be an important domain for social functioning in schizophrenia, but that executive functions are more important for community functioning in bipolar disorder (Laes & Sponheim, 2006).

Mechanisms through Which Cognition Influences Outcome in Schizophrenia Although the connections between cognitive status and daily functioning are clearly documented at this stage, we know relatively little about the mechanisms through which the linkages exist. The identification of mechanisms is important for several reasons. One reason is that it enables investigators to test statistically the adequacy of models of outcome in schizophrenia using techniques such as path analysis and structural equation modeling (Bellack et al., 2007). Given the highly complex nature of community functioning in schizophrenia, and its reliance on a host of clinical, personal, and social factors, it is safe to assume that many of the observed effects between cognition and community activities involve mediators that act between neurocognitive processes and functional outcomes. A second reason to identify mechanisms is that identification of key mediators can also suggest specific therapeutic targets. For example, an identified mediator of functional outcome would be a likely target for intervention in itself, especially if the mediator was thought to be more proximal to the outcome of interest. This situation could exist if a mediator, based on a well-­grounded theoretical model, was thought to be closer to community outcome or vocational success than the basic cognitive process. An important goal in this area is to map out the key connections to outcome to help interpret treatment effects and to suggest new interventions. Researchers have started to propose and test promising mediators between cognitive processes and outcome; two examples are shown in a schematic in Figure 18.1. If mediating variables are included in a model, the direct connections between cognition and functioning (shown as the single arrow with a question mark in the figure) Mediating Variables

Functional Outcome Domains U Social

Social Cognition ?

Basic Cognition Functional Capacity

UOccupational UIndependent Living U Rehabilitation Success

FIGURE 18.1.  Cognition, mediating variables, and functional outcome.

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may remain (in the case of partial mediators) or might disappear (in the case of full mediators). One proposed mediator between cognition and functional outcome is social cognition. Studies of social cognition in schizophrenia research have examined concepts such as social perception, theory of mind, emotion processing, social knowledge and attributional bias (Green, Olivier, Crawley, Penn, & Silverstein, 2005; Penn, Corrigan, Bentall, Racenstein, & Newman, 1997). “Emotional processing” refers broadly to aspects of perceiving and using emotion. For example, an influential model of emotional processing includes four components: identifying emotions, facilitating emotions, understanding emotions, and managing emotions (Mayer, Salovey, & Caruso, 2002). “Theory of mind” typically refers to the ability to infer the intentions and beliefs of others. “Social perception” refers to the ability to judge social roles (e.g., intimacy and status) and social context; the term can also refer to one’s perception of relationships between people, in addition to perception of cues that are generated by a single person. “Social knowledge” (also called “social schema”) refers to the awareness of the rules and goals that characterize social situations and guide social interactions. “Attributional bias” refers to how one explains the causes for positive and negative outcomes and how the meaning of events is based on this attribution of their cause. Numerous reports have linked measures of social cognition to basic (nonsocial) cognition, on the one hand, and functional outcome, on the other (Kee, Green, Mintz, & Brekke, 2003; Kee, Kern, & Green, 1998; Mueser et al., 1996; Penn, Spaulding, Reed, & Sullivan, 1996). More recently, studies have combined all three types of measures into single analyses and have evaluated directly whether aspects of social cognition (e.g., emotion perception and social perception) act as mediators between basic cognitive processes and functional daily outcomes (Addington, Saeedi, & Addington, 2006; Brekke, Kay, Kee, & Green, 2005; Sergi, Rassovsky, Nuechterlein, & Green, 2006; Vauth, Rusch, Wirtz, & Corrigan, 2004). Some of these studies have applied sophisticated statistical procedures, such as structural equation modeling and path analysis, to address the question of mediation. The results so far are promising and consistent: Social cognition appears to be a mediator for functional outcome. At a minimum it seems to be a partial mediator (i.e., significantly reducing the direct relationship between cognition and outcome) and, in some cases, it acts as a complete mediator (i.e., eliminating the direct relationship between cognition and outcome) (Brekke et al., 2005; Sergi et al., 2006). Functional capacity has also been considered as a potential mediator. The term “functional capacity” refers to an individual’s ability to perform key tasks of daily living (Bellack, Sayers, Mueser, & Bennett, 1994; McKibbin, Brekke, Sires, Jeste, & Patterson, 2004). Assessments of functional capacity use simulated activities (e.g., maintaining a social conversation, preparing a meal, taking public transportation, managing medications). These assessments can be conducted in the clinic and do not rely on observing the individual in the community. Good performance on a functional capacity task indicates that the person is capable of performing the tasks, but it does not necessarily mean that he or she will perform the task in the community. Performance of tasks in the community depends on other factors such as opportunity and willingness. Correlations between functional capacity measures and cognitive performance suggest good correspondence between the underlying cognitive skills

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and the functional capacity measure (Addington & Addington, 1999; Bellack et al., 1994; Klapow et al., 1997). A recent study using path analysis examined a measure of functional capacity, the UCSD Performance-Based Skills Assessment, that involved simulations of daily activities such as managing finances, shopping in a grocery store, and using public transportation (Patterson, Goldman, McKibbin, Hughs, & Jeste, 2001). This functional capacity measure was related to both basic cognition and community outcome, and acted as a mediator between the two for each of the functional outcome domains that were examined (Bowie, Reichenberg, Patterson, Heaton, & Harvey, 2006).

Stimulating the Development of New Cognition-­Enhancing Drugs for Schizophrenia Because of the findings that cognition is related to community functioning in schizophrenia, as well as the evidence that cognition is a core feature of schizophrenia, cognition has become a treatment target. In essence, cognition lies at the root of a major public health concern: the fact that patients with schizophrenia experience high levels of disability and have difficulty achieving acceptable goals when entering the community. A common opinion is that the antipsychotic medications may have reached the limits of their treatment potential. For these reasons, the development of new drugs to enhance cognitive functioning in schizophrenia has become both a scientific focus and a public health priority. However, as of a couple years ago, there were notable obstacles that prevented any drug from receiving Food and Drug Administration (FDA) approval for this purpose (Marder & Fenton, 2004). First, there was no consensus on how to measure cognitive performance as an endpoint in clinical trials. The FDA had received inquiries over the years from companies that wanted to obtain approval for potential cognition­enhancing drugs. But each company used different definitions and measurements of cognition, a situation that the FDA found unacceptable. It was essential to find an endpoint for clinical trials that was based on a broad, interdisciplinary consensus process. Other significant barriers to drug development for cognitive enhancement in schizophrenia included the lack of a consensus regarding the appropriate design of clinical trials. For example, subject selection criteria, phase of illness, length of the trials, and ways to manage potential drug–drug interactions all required a consensus before the FDA was willing to moving forward. Other obstacles involved the prioritization of neuropharmacological targets (e.g., which receptor targets are the most promising) and criteria to evaluate promising compounds. Given the overriding ambiguity involving methods and measurements, and with the absence of any pathway for FDA approval, the pharmaceutical industry was understandably reluctant to make a substantial investment in the development of cognition-­enhancing drugs for schizophrenia. To resolve this situation and to stimulate the development of new drugs for cognition enhancement in schizophrenia, the National Institute of Mental Health (NIMH) launched the MATRICS (Measurement and Treatment Research to Improve Cognition in Schizophrenia) initiative. The mandate of MATRICS was to address the barriers to drug approval by holding a series of consensus meetings (with representa-

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tives of industry, academia, and government). MATRICS was charged with building a pathway for drug approval by reaching consensus on the methods and measures that would be used to evaluate promising new cognition-­enhancing drugs for schizophrenia. The expectation was that once a pathway for drug approval was created, it would motivate the pharmaceutical industry to invest their resources and to develop drugs for cognitive enhancement in schizophrenia. This movement from industry, in fact, seems to be occurring. An essential product of the NIMH-MATRICS Initiative was a consensus cognitive battery that would be the standard outcome measure for all clinical trials of cognition-­enhancing drugs for schizophrenia. Selection of the consensus cognitive battery involved a thorough, multistep process consisting of several consensus meetings, evaluation, discussion, and finally a data-­collection component (Green, Nuechterlein, et al., 2004). Essential criteria for the final selection of tests included (1) high test–­retest reliability, (2) high utility as a repeated measure, (3) demonstrated relationship to functional outcome, and (4) demonstrated tolerability (acceptable to patients) and practicality (acceptable to testers). A relationship to functioning was selected as one of the essential criteria because part of the rationale of MATRICS was the linkage between cognitive and community functioning. It is clearly hoped that drug-­related improvements in cognition will eventually lead to improvements in functioning. The components of the MATRICS Consensus Cognitive Battery (MCCB) are shown in Table 18.1. The MCCB is distributed by Harcourt Assessment (San Antonio, TX), Multi-­Health Systems (Toronto), and Psychological Assessment Resources (Lutz, FL). To evaluate the MCCB and other products of MATRICS, the NIMH recently launched another initiative to develop a clinical trials network called Treatment Units for Neurocognition and Schizophrenia (TURNS). TURNS is a network of seven aca-

TABLE 18.1.  MATRICS Consensus Cognitive Battery Domain

Test

Speed of processing

Brief Assessment of Cognition in Schizophrenia (BACS)—SymbolCoding Category Fluency (Animal Naming) Trail Making Part A

Attention/vigilance

Continuous Performance Test-Identical Pairs

Working memory (nonverbal)

Wechsler Memory Scale (WMS)-III—Spatial Span

Working memory (verbal)

University of Maryland—Letter–Number Span

Verbal learning

Hopkins Verbal Learning Test—Revised

Visual learning

Brief Visuospatial Memory Test—Revised

Reasoning and problem solving

Neuropsychological Assessment Battery (NAB)—Mazes

Social cognition

Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT)— Managing Emotions

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demic research sites that is dedicated to identifying, obtaining, and testing the efficacy of new drugs to improve cognition in schizophrenia. This network was formed to validate the clinical trial methodology recommended by MATRICS (Buchanan et al., 2005) by conducting two or three trials that will be supported initially by NIMH. Later TURNS is expected to become self-­sufficient and to obtain funds through private and federal sources. TURNS was designed as a “fast track” for evaluating promising compounds and has received numerous nominations from companies of potential compounds. The first TURNS trials started in early 2007.

Summary This chapter has briefly summarized several topics related to cognitive performance in schizophrenia. First the evidence that cognitive performance is a core feature of schizophrenia was reviewed. It is safe to view cognitive deficits as part of the illness and not secondary to clinical symptoms or to medications. Next the literature on the relationship of cognitive performance to functioning in schizophrenia was summarized. The literature is quite large and highly consistent in showing relationships between cognitive performance and community functioning. The strengths of the relationships are medium for individual domains and large for summary scores, which indicate that much of the variance in functional outcome lies beyond cognition. Once such relationships have been demonstrated, other questions start to emerge. One question involves the mechanisms for such relationships. It is important, for both scientific and intervention reasons, to identify key mediators that act between cognition and community functioning. So far, two promising mediators have been suggested: social cognition and functional capacity. These two constructs have been shown to be related to both cognitive performance and functioning, both reduce (or eliminate) the direct connection, and both add to the goodness of fit when added to models of outcome. The final question is whether cognition can be a target for intervention. The NIMH launched two initiatives. The first one (MATRICS) was charged with building a pathway for drug approval through a series of consensus meetings and by development of a consensus battery. The second one (TURNS) is a clinical trial network that is currently testing the products of MATRICS. In this chapter we have focused on the efforts to develop new psychopharmacological treatments for cognitive enhancement in schizophrenia, but other efforts are occurring in cognitive remediation for schizophrenia. In all likelihood true advances in community outcome for patients with schizophrenia will occur only when cognition-­enhancing drugs are combined with nonpharmacological approaches.

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Future Directions in the Assessment of Everyday Functioning Thomas D. Marcotte and Igor Grant

A

s noted throughout this volume, the neuropsychological approach to predicting everyday functioning has many advantages, including a legacy of developing standardized measures that are well characterized with respect to validity and reliability, and a rich literature addressing the relationships between brain function and real world-­performance. But, as also summarized in numerous chapters, this approach still has limitations. Here, based on the material presented in this book, we briefly provide recommendations for future work linking brain function to real-world functioning. 1.  Foster development, and implementation, of new measures with greater ecological validity. This call has gone out for decades (Heaton & Pendleton, 1981). While we can continue to refine our understanding of how tools initially developed to localize brain lesions and aid in neurological/neuropsychiatric diagnosis might predict real-world functioning, it is also worth considering new paradigms. Rather than starting with circumscribed behaviors that have been well delineated in the controlled laboratory and trying to extrapolate findings to real-world scenarios, it might prove fruitful to develop new measures whose design begins with observations of human behavior in the real world, in all of its complexity (Burgess et al., 2006; Kingstone, Smilek, & Eastwood, 2008). This may ultimately lead to new constructs regarding how we attend to, prioritize, and manage our complex lives, and also a better understanding of the component processes at work during complicated activities. This work might be furthered by new technologies, such as virtual reality, which is increasingly sophisticated and less costly and may provide interesting opportunities for studying behaviors in a seminaturalistic manner. These methods may be particularly useful in areas such as the assessment of driving abilities, where other approaches (e.g., on-road assessments), are time-­consuming, costly, and dangerous. However, “virtual” is not synonymous with “ecologically valid,” and such instru-



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ments should be held to the same standards of reliability and validity as traditional paper-and-­pencil and computerized tests. It is our opinion, as well as that of others (e.g., Buchanan et al., 2005; Laughren, 2001), that functional outcomes should be a key factor in determining whether there is benefit in pharmaceutical and behavioral treatments. Unfortunately, there are limited options with respect to measures that fit within a clinical trial protocol, where time is often limited and per-site costs can significantly impact feasibility. Many trials thus rely upon brief, traditional neuropsychological tests to examine cognition and infer functional outcomes. While cognitive functioning itself is clearly an important outcome, as shown in this volume it may not always translate to everyday performance. How can development and implementation of new measures be facilitated? Unfortunately, the current approach of relying on individual investigators/research teams to develop, refine, and apply new everyday functioning instruments to multiple populations is often inefficient. It is difficult to lay a foundation for industry standards when careers rely upon external funding and publishing, activities that almost universally require “novel” experiments and findings. As a result, although some measures have developed a wide following (e.g., Rivermead Behavioral Memory Test, Test of Everyday Attention, Six Elements Test), in many cases there is limited understanding of how measures designed to predict real-world functioning perform across different clinical groups and, importantly, few studies aim to replicate previous findings. Confidence in such measures would be greater if findings were examined repeatedly within similar patient groups and across different patient groups. In addition to the single, “investigator-­initiated” approach (which admittedly may best promote creativity), research can also be significantly advanced by the development of common methodologies that yield predictor or outcome measures that serve as standards to which other approaches can be compared. For example, if there were commonly accepted, valid instruments that predicted real-world functioning across different samples and studies, one would be able to compare the value of different interventions, as well as the relationship between brain function and everyday performance between these groups. Such a step is likely only to occur with institutional support. It is thus our recommendation that potential stakeholders, such as the National Institutes of Health (NIH), convene expert panels to advance the development of standardized measures for assessing everyday functioning abilities. One potential high-­impact focus would be developing a consensus everyday functioning battery for use in clinical trials. The NIH is undertaking a rigorous approach to developing cognitive and other assessments as part of the NIH Toolbox (www.nihtoolbox.gov), in which experts were formally surveyed, and investigators then selected or developed measures that would constitute a brief battery usable in clinical trials and other research. This effort does not include functional measures. In schizophrenia research, the National Institute of Mental Health supported a project that involved surveys of experts and focused conferences to define a standard cognitive battery for use in clinical trials (MATRICS; Nuechterlein et al., 2008); this effort also included the identification of “co-­primary” measures that would be functionally meaningful (Green et al., 2008). While time will tell whether these efforts advance clinical research and treatment, a similar approach, perhaps on a more modest scale, might be undertaken to carefully iden-

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tify, modify, or even develop everyday functioning measures that are appropriate for multiple research and clinical situations. This would likely result in more widespread use of such instruments, and facilitate comparisons across different treatments. This approach has its limitations—“consensus” does not always equate to “best”—and not all investigators would be pleased with the selected approaches and measures, but it would likely provide a needed impetus to further the cause of addressing functional outcomes in research. 2.  Improve methods for directly measuring “real-world” outcomes. Operationalization of “real-world outcomes” is not necessarily any more advanced than the predictors being used to predict such outcomes. In order to develop useful laboratorybased measures, we need gold standards regarding what defines functioning in the real world. To establish how individuals are functioning in their daily lives, investigators/clinicians often rely on reports from patients and informants, which, while very important, have limitations, as noted in previous chapters. Developing technology may help. For example, the advent of miniaturized, unobtrusive tools (e.g., webcams) to record behavior as it occurs in the wild may significantly improve our ability to observe and measure an individual’s performance under common demands/distractions. Such an approach, for example, was demonstrated in the “100-car naturalistic study” (Neale, Dingus, Klauer, Sudweeks, & Goodman, 2005), in which investigators added monitoring equipment (including videocameras) and tracked driving behavior over the course of a year. New, novel approaches to establishing the naturalistic evaluation of an individual’s abilities are still needed. 3.  Develop algorithms for predicting everyday performance based on the contributions of neuropsychological and non-­neuropsychological factors. Factors such as personality/temperament, psychiatric conditions (especially depression), licit and illicit substance use, medications, disease, psychosocial factors, environmental conditions, literacy, idiosyncratic approaches to daily life, and so on, no doubt explain different amounts of variance regarding performance of everyday tasks. Predictions will have better validity (and real life meaning) if the major contributors to everyday functioning can be given appropriate weight as to their likely importance in a particular situation. For example, a prospective memory difficulty may predict everyday performance in particular areas, but in some circumstances there may be “higherorder” cognitive–­dispositional–­motivational complexes that determine even more of the variation in functioning, and the extent to which prospective memory problems matter. Thus, some people have trouble initiating behaviors (e.g., due to obsessional traits, or basal ganglia pathology, such as in Parkinson’s disease), others may have disinhibition, some have decisional difficulties, and still others may have altered reward contingencies that affect their motivation. Attitudinal (e.g., sense of optimism) and coping (e.g., problem-­solving approach, sense of mastery) variables may be powerful moderators of the path from cognitive changes to successful real-world performance. The neural bases of such complex behaviors and dispositions are only now being mapped. Disturbance in these in some instances may reflect a common pathological process that expresses itself also as a specific neuropsychological deficit; or it may reflect a long-­standing disposition, or the superimposition of another problem such as mood disorder. Fatigue and pain are added features of many chronic diseases that have central nervous system injury as a component, and these can amplify everyday

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difficulties. Research into how such factors separately and jointly affect everyday functioning is sorely needed in order to create clinically useful profiles and prediction estimates. Development of such multivariate models will also facilitate clinical decisions into where initial interventions might be most profitably directed. 4.  Address cultural issues when developing, and interpreting, everyday functioning measures. Many everyday tasks are universal, and are required for successful functioning in most societies; but they can also differ substantially from culture to culture (Cherner, Chapter 8, this volume). In order to determine the effects of diseases and brain dysfunction across cultures, it would be ideal to standardize instruments as much as possible, but this may be neither easy nor appropriate. Akin to assessing whether culture-­specific norms are needed for neuropsychological tests, the field may need to develop culture-­specific norms for everyday functioning measures. This is true even within societies. For example, Spanish speakers in the United States may have different methods of money management and cooking than native English speakers. In some cases, particularly when individuals have very little or no education, measures of functional ability may prove to be the best way to determine whether cognitive decline has occurred. 5.  Pursue studies examining the neural basis of real-world functioning. While the last few decades have seen dramatic progress in our ability to relate cognitive performance to brain function, this has typically been accomplished by using behavioral measures far removed from real-world tasks (Burgess et al., 2006). What are the neural underpinnings of perceiving, attending to, and making decisions in complex environments? There are a small, but growing, number of studies examining components of real-world functioning while individuals are in a scanner (e.g., Just, Keller, & Cynkar, 2008; Simons, Scholvinck, Gilbert, Frith, & Burgess, 2006), but such work is often constrained by technical limitations. A relatively new field in human factors, neuroergonomics (Parasuraman & Rizzo, 2006), focuses on using neuroimaging techniques (e.g., functional magnetic resonance imaging, electroencephalography) to capture real-time brain function during different activities; in some work-­related studies, a high workload then initiates adaptive automation in which functions are distributed between human and machine. This work has been applied to military and clinical situations (Parasuraman & Wilson, 2008), and may ultimately inform future clinical studies. 6.  Translate research/clinical findings into results relevant to the individual. Most studies involve null hypothesis significance testing in order to determine if there is a statistically significant difference between groups with and without a given brain condition. To be most clinically useful, measures of everyday functioning should help clinicians and researchers identify individuals at risk for impaired real-world functioning. We thus recommend that whenever possible studies report classification accuracy statistics. This includes not only the more traditional measures of sensitivity, specificity, and overall accuracy (hit rate), but even more clinically relevant measures such as positive predictive value (chance that someone who is impaired on a laboratory-based test also has impaired everyday functioning), negative predictive value (chance that if someone was unimpaired on the laboratory-based measure that he or she is also unimpaired in real-world functioning), and risk ratios (e.g., likelihood and odds ratios) (Woods, Weinborn, & Lovejoy, 2003). As such data expand

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across many patient populations, the information will help us better understand the utility, and universality, of different approaches.

References Buchanan, R. W., Davis, M., Goff, D., Green, M. F., Keefe, R. S., Leon, A. C., et al. (2005). A summary of the FDA–NIMH–MATRICS workshop on clinical trial design for neurocognitive drugs for schizophrenia. Schizophrenia Bulletin, 31(1), 5–19. Burgess, P. W., Alderman, N., Forbes, C., Costello, A., Coates, L. M., Dawson, D. R., et al. (2006). The case for the development and use of “ecologically valid” measures of executive function in experimental and clinical neuropsychology. Journal of the International Neuropsychological Society, 12(2), 194–209. Green, M. F., Nuechterlein, K. H., Kern, R. S., Baade, L. E., Fenton, W. S., Gold, J. M., et al. (2008). Functional co-­primary measures for clinical trials in schizophrenia: Results from the MATRICS Psychometric and Standardization Study. American Journal of Psychiatry, 165(2), 221–228. Heaton, R. K., & Pendleton, M. G. (1981). Use of neuropsychological tests to predict adult patients’ everyday functioning. Journal of Consulting and Clinical Psychology, 49(6), 807–821. Just, M. A., Keller, T. A., & Cynkar, J. (2008). A decrease in brain activation associated with driving when listening to someone speak. Brain Research, 1205, 70–80. Kingstone, A., Smilek, D., & Eastwood, J. D. (2008). Cognitive ethology: A new approach for studying human cognition. British Journal of Psychology, 99(Pt. 3), 317–340. Laughren, T. (2001). A regulatory perspective on psychiatric syndromes in Alzheimer disease. American Journal of Geriatric Psychiatry, 9(4), 340–345. Neale, V. L., Dingus, T. A., Klauer, S. G., Sudweeks, J., & Goodman, M. (2005). An overview of the 100-Car naturalistic study and findings (No. 05-0400). Washington, DC: National Highway Traffic Safety Administration. Nuechterlein, K. H., Green, M. F., Kern, R. S., Baade, L. E., Barch, D. M., Cohen, J. D., et al. (2008). The MATRICS Consensus Cognitive Battery, part 1: Test selection, reliability, and validity. American Journal of Psychiatry, 165(2), 203–213. Parasuraman, R., & Rizzo, M. (2006). Neuroergonomics: The brain at work. New York: Oxford University Press. Parasuraman, R., & Wilson, G. F. (2008). Putting the brain to work: Neuroergonomics past, present, and future. Human Factors, 50(3), 468–474. Simons, J. S., Scholvinck, M. L., Gilbert, S. J., Frith, C. D., & Burgess, P. W. (2006). Differential components of prospective memory?: Evidence from fMRI. Neuropsychologia, 44(8), 1388–1397. Woods, S. P., Weinborn, M., & Lovejoy, D. W. (2003). Are classification accuracy statistics underused in neuropsychological research? Journal of Clinical and Experimental Neuropsychology, 25(3), 431–439.

Index

Page numbers followed by f indicate figure, t indicate table

Ability, compared to function, 14–19 Academic performance, 7–8 Acetylcholinergic therapies, 284 Acquired brain dysfunction, 311. See also Traumatic brain injury (TBI) Activities of daily living (ADLs) aging and, 253–256 Cognitive Functional Evaluation (CFE) and, 72 cross-cultural assessment and, 212–213, 214, 215, 217–218 dementia and mild cognitive impairment (MCI) and, 264, 268–269 depression and, 429 HIV infection and, 394–395, 395f human factors/ergonomics (HF/E) and, 49–51 multiple sclerosis and, 366–368 occupational therapy approach and, 81 overview, 8–9 psychiatric disorders and, 16–17 schizophrenia and, 444–449, 447f traumatic brain injury and, 307–310 vascular dementia and, 280, 288–296, 291t vocational functioning and, 125 Activity Card Sort (ACS), 70, 81 Adaptive automation, 57–58. See also Automaticity Adherence, medication. See Medication adherence Adjustment multiple sclerosis and, 375 traumatic brain injury and, 319 vocational functioning and, 319 ADL Checklist for Neglect occupational therapy approach and, 79–80 overview, 75



Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study, 257 Age-related factors MS and vocational functioning and, 373 sports-related mild traumatic brain injury and, 338–339 Aggression, automobile driving and, 179–180 Aging automobile driving and, 170–171, 270–271 cognitive changes and, 250–253 daily functioning and, 253–256 depression and, 428 falls and, 255–256, 270 future directions in, 257–258 health-related quality of life and, 237–241, 239f, 242f living alone/wandering and, 270 medication management/adherence and, 140–143, 157–158, 271–272 memory and, 24 overview, 248 physiology of, 248–250 schizophrenia and, 237–238 training and, 256–257 See also Alzheimer’s disease; Dementia Alcohol use automobile driving and, 180, 183 traumatic brain injury and, 304 See also Substance use Alertness, automobile driving and, 180–181 Allen Cognitive Level Screen–5 (ACLS-5), 71, 71t Alzheimer’s disease automobile driving and, 10–11, 184–185, 185f cognitive functioning and, 287–288

463

464 Alzheimer’s disease (cont.) health-related quality of life and, 238–241, 239f, 242f instrumental activities of daily living (IADLs) and, 8, 9 medication adherence and, 144 memory and, 68–69 self-report measures and, 97 vascular dementia and, 285 See also Dementia of the Alzheimer’s type (DAT) Amnesia, automobile driving and, 181 Amygdala, vascular dementia and, 284 Amyloid cascade hypothesis, 268 ANAM (Automated Neuropsychological Assessment Metric), 342 Anger, automobile driving and, 179–180 Anterograde memory, 305. See also Memory Anticonvulsants, automobile driving and, 183 Antidepressants automobile driving and, 183 medication management/adherence and, 430 overview, 422–423 Antihistamines, 183 Antihypertensives, 183 Antilipemics, 183 Antipsychotic medications health-related quality of life and, 237–238 schizophrenia and, 442–444 Antisocial personality disorder, 180 Anxiety medication adherence and, 150–152 traumatic brain injury and, 310, 319 vocational functioning and, 319 Apathy, 293–294 ARGOS (Automobile for Research in Ergonomics and Safety), 186–189, 187f Arousal, 180–181 Assessment automobile driving and, 173–179, 174t, 175f, 183–195, 185f, 187f, 190f, 194t, 314–315 cognitive disabilities model and, 69–78, 71t, 76t, 77t defining “everyday functioning” outcomes and, 12–14 of dementia and mild cognitive impairment (MCI), 266–267 distinction between function and ability, 14–19 functional skills training and, 74t future directions in, 457–461 human factors/ergonomics (HF/E) and, 43–47, 45f, 46t of instrumental activities of daily living (IADLs), 96–105, 309 occupational therapy approach and, 65–66, 69–78, 74t on-field and sideline management of sportsrelated mild traumatic brain injury and, 334–340, 334t, 335t, 336t, 338t

Index overview, 5–6 schizophrenia and, 443 sports-related mild traumatic brain injury and, 345–347, 347f traumatic brain injury and, 309, 345–347, 347t vascular dementia and, 290–291 vocational functioning and, 9–10 See also Neuropsychological testing Assessment of Awareness of Disability (AAD), 76 Assessment of Motor and Process Skills (AMPS) instrumental activities of daily living (IADLs) and, 99 overview, 73–74, 74t Attentional blink, 178 Attentional functioning aging and, 252–253 automobile driving and, 10–11, 55, 177–179, 313 cognition and, 65 functional skills training and, 67 human factors/ergonomics (HF/E) and, 42t medication adherence and, 141 multiple sclerosis and, 361–362 neuropsychological testing instruments that focus on, 21 traumatic brain injury and, 304–305 Attention-deficit/hyperactivity disorder (ADHD), 75 Auditory functioning cognitive disabilities model and, 68–69 human factors/ergonomics (HF/E) and, 42t Autobiographical Memory Interview, 21 Automaticity, 17, 48, 57 Automobile driving. see also Navigational functioning aging and, 252–253, 256 arousal, alertness, and fatigue and, 180–181 assessment and, 173–179, 174t, 175f, 183–195, 185f, 187f, 190f, 194t attentional functioning and, 252–253 conceptual framework, 168–170, 169f dementia and mild cognitive impairment (MCI) and, 270–271 depression and, 431–432 ecologically oriented measures and, 25 emotions and personality and, 179–180 executive functioning and, 173–179, 174t, 175f HIV infection and, 402–405, 403f, 404f human factors/ergonomics (HF/E) and, 54–56 medication effects and, 183 multiple sclerosis and, 369–372 overview, 7–8, 10–11, 168, 195–196 public policy and, 192–195, 194t schizophrenia and, 446 sensation and perception and, 170–172, 172f traumatic brain injury and, 304, 313–317, 320–321 vascular dementia and, 294–295 Autonomy, medication adherence and, 153–154 Average Impairment Rating, 11

Index Awareness automobile driving and, 313 traumatic brain injury and, 306–307, 308, 319, 321 vocational functioning and, 319 AX-Continuous Processing Task, 174t Axon loss multiple sclerosis and, 358 vascular dementia and, 284

B Background information, 70, 71t Bálint’s syndrome, 171 Barthel Index (BI), 366 Basal forebrain, 284 Baseline status tests, 71–72 Basic activities of daily living (BADLs) assessment and, 97 HIV infection and, 394–395, 395f overview, 93–94 vascular dementia and, 288–296, 291t Bayer Activities of Daily Living Scale (BADLS), 97 Beck Depression Inventory, 364 Behavior Rating Inventory of Executive Function, 21 Behavioral Assessment of Dysexecutive Syndrome occupational therapy approach and, 74t, 80 overview, 21 Behavioral Assessment of Vocational Skills, 128–129 Behavioral factors, predicting vocational functioning and, 126 Behavioral Inattention Test occupational therapy approach and, 74t overview, 21, 74 Behavioural Assessment of Dysexecutive Syndrome (BADS) overview, 74 traumatic brain injury and, 306 Benton Visual Retention Test (BVRT), 193 Biases clinician ratings in assessment and, 13 instrumental activities of daily living (IADLs) and, 105 self-report measures and, 12–13 Biological factors, 137–138 Bipolar disorder overview, 420 schizophrenia and, 446–447 Block Design, 292 Boston Diagnostic Aphasia Examination, 107 Boston Naming Test instrumental activities of daily living (IADLs) and, 106 vascular dementia and, 288 Brain imaging technologies. See Neuroimaging

465 Brain injury automobile driving and, 10–11 instrumental activities of daily living (IADLs) and, 8 vocational functioning and, 9–10 Brief Symptom Inventory (BSI), 240 Brief Test of Attention, 21 Brief Visual Memory Test—Revised (BVMT-R), 375–376 Brown–Peterson Test of Short Term Memory, 374

C California Verbal Learning Test HIV infection and, 401–402 medication adherence and, 147, 148f multiple sclerosis and, 367 overview, 19–20 predicting vocational functioning and, 128 Canadian Occupational Performance Measure (COPM), 70 Cardiovascular Health Cognition Study, 286–287 Caregiver burden, 272–273 Catecholamine hypothesis of depression, 423–424 CDR–Sum of Boxes (CDR-SB), 267 Center for Epidemiological Studies Depression Scale (CES-D), 364 Central nervous system, 391 Cerebral akinetopsia, 171 Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), 284–285 Cerebral hemispheres, 284 Cerebral lesions, 171, 178 Cerebrovascular disease (CVD) activities of daily living (ADLs) and, 288–296, 291t frequency of, 285–286 overview, 283 Change, element of, 95 Chicago Multiscale Depression Inventory (CMDI), 364 Cholinergic system automobile driving and, 181 vascular dementia and, 284 Cholinesterase inhibitor, 297 Client-centered approach, 63–64 Clinical Dementia Rating (CDR) automobile driving and, 193 overview, 267 Clinical Global Impressions of Change (CGIC), 267 Clinical interviews attentional functioning and, 21 Cognitive Functional Evaluation (CFE) and, 70, 71t Clinical judgment, 105

466 Clinician ratings in assessment instrumental activities of daily living (IADLs) and, 101 overview, 13 Clinician’s Interview-Based Impression of Change Plus Caregiver Input (CIBIC-Plus), 267 Clock Drawing Test multiple sclerosis and, 377 overview, 71t Cognistat, 71t, 71–72 Cognition, 65 Cognitive determinants of functioning, 15–16 Cognitive disabilities model assessment and, 69–78, 71t, 74t, 77t rehabilitation in dementia and, 68–69 Cognitive flexibility, 102–103 Cognitive Functional Evaluation (CFE) overview, 70 stages in the development of, 70–76, 71t, 74t, 77t Cognitive functioning aging and, 250–253 automobile driving and, 10–11, 11, 168–169, 191–192, 313, 315–316 cultural differences in, 56–57 depression and, 424–429 human factors/ergonomics (HF/E) and, 42t medication adherence and, 140–142, 157 MS and vocational functioning and, 373–374 multiple sclerosis and, 360–364, 374, 375 neuropsychological testing and, 19–20 overview, 7–8 predicting vocational functioning and, 127 schizophrenia and, 442–443 traumatic brain injury and, 304–307 vascular dementia and, 287–288 vocational functioning and, 51–52, 117–122, 118f, 119f, 121f, 374 Cognitive impulsiveness, 176. See also Impulse control Cognitive orientation to daily occupational performance (CO-OP) occupational therapy approach and, 64–67 theoretical foundations for, 65–66 Cognitive Performance Test (CPT) instrumental activities of daily living (IADLs) and, 99–100 overview, 72, 74t Cognitive reserves instrumental activities of daily living (IADLs) and, 102 overview, 17, 249–250 Cognitive screening, 71–72 Cognitive theoretical models, 64–69, 71t, 74t Cognitive-behavioral therapy depression and, 423 multiple sclerosis and depression and, 365 CogState, 342 Collateral information in assessment, 13 Color–Word Interference test, 295 Community Integration Questionnaire (CIQ), 309

Index COMPASS, 129–130 Compensatory strategies medication management and, 157 neuropsychological testing and, 18–19 Complex attention, 305. See also Attentional functioning Computed tomography (CT). See Neuroimaging Computer-aided risk (CAR) simulator multiple sclerosis and, 369–370 overview, 184–186, 185f Computer-based neuropsychological testing, 342–344. See also Neuropsychological testing Concussion in sports. See Sports-related mild traumatic brain injury Concussion in Sports (CIS) panel, 332 Confounding factors, 121–122 Construct validity, 211–215 Contextual factors, 77, 77t Contextual Memory Test, 75 occupational therapy approach and, 74t overview, 21 Continuous positive airway pressure (CPAP) therapy, 181 Controlled Oral Word Association Test automobile driving and, 174t, 193 multiple sclerosis and, 374, 375 Coping skills multiple sclerosis and depression and, 365 traumatic brain injury and, 319 vocational functioning and, 319 Cortex, 284 Countermeasures, automobile driving and, 191–192 Cross-cultural assessment adaptation process, 210–211 construct validity and, 211–215 ecological validity and, 213–215, 216f example of, 215, 217–218 overview, 209, 218–219 CT scan. See Neuroimaging Cultural factors assessment of functional abilities and, 209–219 future directions in assessment and, 460 human factors/ergonomics (HF/E) and, 56–57

D Decision-making skills automobile driving and, 173–175, 174t, 175f functional skills training and, 67–68 human factors/ergonomics (HF/E) and, 42t Declarative memory, 251–252. See also Memory Deductive Reasoning (DR), 75 Delis–Kaplan Executive Functioning System (D-KEFS), 295 Dementia activities of daily living (ADLs) and, 268–269 automobile driving and, 194, 270–271 caregiver burden and, 272–273

Index clinical assessment of, 266–267 cognitive disabilities model and, 68–69 depression and, 425 falls and, 270 future directions in, 273–274 health-related quality of life and, 238–241, 239f, 242f HIV infection and, 392 instrumental activities of daily living (IADLs) and, 102–103 living alone/wandering and, 270 medication management/adherence and, 143–145, 271–272 multiple sclerosis and, 363–364 overview, 264 self-report measures and, 96–97 See also Dementia of the Alzheimer’s type (DAT); Mild cognitive impairment (MCI); Vascular dementia Dementia of the Alzheimer’s type (DAT) activities of daily living (ADLs) and, 269 clinical assessment of, 266–267 diagnostic criteria for, 264–265 future directions in, 273–274 mild cognitive impair (MCI) and, 265–266 neuropathology and genetics of, 268 neuropsychological testing and, 266–267 overview, 264 See also Alzheimer’s disease; Dementia Dementia Rating Scale, 102–103 Demographic factors, automobile driving and, 192 Demyelination, 357–358. See also Multiple sclerosis Depression activities of daily living (ADLs) and, 429 automobile driving and, 180, 431–432 caregiver burden and, 272 cognitive functioning and, 424–429 future directions in, 433 HIV infection and, 432 medication management/adherence and, 150–152, 430 medications and, 183, 422–423 multiple sclerosis and, 358, 365, 375 neurobiology/neuroanatomy of, 423–424 neuropsychological testing and, 16–17 overview, 419–420, 420f, 433 prevalence of, 421–422 psychiatric disorders and, 432–433 psychosocial functioning and, 429–430 quality of life and, 432 traumatic brain injury and, 304, 310, 319 treatment of, 422–423, 426–427 vocational functioning and, 319, 430–431 Design Fluency, 174t Diagnosis dementia of the Alzheimer’s type (DAT) and, 264–265 instrumental activities of daily living (IADLs) and, 94, 106

467 mild cognitive impair (MCI) and, 265 multiple sclerosis and, 358–360 neurocognitive impairment associated with HIV and, 392–393 overview, 6 vascular dementia and, 281–284, 282t Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) overview, 94 vascular dementia and, 282t, 283–284 Dictionary of Occupational Titles (DOT), 114 Digit Span, vascular dementia and, 290–291, 292 Direct Assessment of Functional Status (DAFS) cross-cultural assessment and, 215, 217–218 instrumental activities of daily living (IADLs) and, 98–99 overview, 21 Disability compared to impairment, 119–120 cross-cultural assessment of, 209–219 distinction between function and ability, 14 Disability adjusted life years (DALYs), 421 Disability Assessment for Dementia (DAD), 97 Disability Rating Scale, 309 Discriminant function analysis, 107–108 Disease Management Assistance System, 398–399 Documentation in assessment automobile driving and, 184 overview, 13–14 Dorsal visual pathway lesions, 171 Dorsolateral prefrontal cortex (DLPFC), 346–347, 347f Driving Habits Questionnaire, 192 Driving issues. see Automobile driving Driving simulators multiple sclerosis and, 369–370 overview, 184–186, 185f Drowsiness, automobile driving and, 180–181. see also Fatigue Drug use automobile driving and, 179–180, 183 medication adherence and, 149–150 traumatic brain injury and, 304 See also Substance use Dynamic Occupational Therapy Cognitive Assessment for Children (DOTCA), 71t, 72 Dysexecutive Questionnaire, 21

E Ecological validity cognitive tests in MS, 366 cross-cultural assessment and, 213–215, 216f future directions in assessment and, 457–458 neuropsychological testing instruments that focus on, 20–25 overview, 6–7

468 Ecologically oriented measures challenges in developing, 24–25 combining with traditional assessments, 25 future directions in, 26–28 overview, 20–25 Educational levels aging and, 251 neuropsychological testing and, 18 Electronic measuring devices, 138–139 Emotional adjustment traumatic brain injury and, 319 vocational functioning and, 319 Emotions, 179–180 Employment human factors/ergonomics (HF/E) and, 51–53 overview, 9–10 See also Vocational functioning Enhanced activities of daily living (EADLs), 49– 51 Environment of testing, 15 Environmental factors human factors/ergonomics (HF/E) and, 42t neuropsychological testing and, 16 Environmental Status Scale (ESS), 366, 367 Environmental supports human factors/ergonomics (HF/E) and, 48 vocational functioning and, 52–53 Episodic memory, 252. See also Memory Epworth Sleepiness Scale, 181, 192 Ergonomics. See Human factors/ergonomics (HF/E) EuroQol (EQ-5D), 227f Excitatory amino acid (EAA)–induced ionic shifts, 333 Executive Function Performance Test (EFPT) multiple sclerosis and, 368 overview, 75–76 Executive functioning aging and, 252 automobile driving and, 11, 173–179, 174t, 175f, 313 depression and, 426–427, 427 medication adherence and, 141, 144–145 multiple sclerosis and, 362 neuropsychological testing and, 21, 22–23 predicting vocational functioning and, 127 traumatic brain injury and, 306 Expanded Disability Status Scale (EDSS), 366

F Face validity. See Ecological validity Falls aging and, 255–256 dementia and mild cognitive impairment (MCI) and, 270 FAMS QOL questionnaire, 376–377

Index Fatigue automobile driving and, 180–181, 370 multiple sclerosis and, 370, 375 sports-related mild traumatic brain injury and, 335 traumatic brain injury and, 309 Federation Internationale de Football Association (FIFA), 332 Financial Capacity Instrument (FCI), 312 Financial management overview, 7–8 traumatic brain injury and, 310–313, 320–321 FLAIR acquisition sequence, 292 Focus groups, 43 Focused attention, 178. See also Attentional functioning Frontal lobe activity, 178–179 Frontal lobe impairments, 67 Frontal lobe lesions, 23 Frontal lobe systems dementia and, 269 multiple sclerosis and, 358 Frontal lobe theory, 250 Frontal Systems Behavior Scale (FrSBe), 269 Frontothalamic circuits, 284 Frustration, automobile driving and, 179–180 Function, compared to ability, 14–19 Functional Activities Questionnaire (FAQ), 97 Functional assessment cross-cultural assessment, 209–219 traumatic brain injury and, 321, 322t vascular dementia and, 290 vocational functioning and, 21 Functional Assessment Measure (FAM) automobile driving and, 314 multiple sclerosis and, 376–377 traumatic brain injury and, 309, 314 Functional Assessment of Cancer Therapy (FACT), 210 Functional Assessment of Chronic Illness Therapy (FACIT), 211–212 Functional Assessment of Multiple Sclerosis (FAMS), 368 Functional Assessment Staging Test (FAST), 267 Functional Behavior Profile questionnaire, 368 Functional capacity, schizophrenia and, 448–449 Functional Deficit Score (FDS), 394 Functional deficits, distinction between function and ability, 14 Functional Independence Measure (FIM) automobile driving and, 314 overview, 79 traumatic brain injury and, 309, 314 vascular dementia and, 295–296 Functional magnetic resonance imaging (fMRI). See Neuroimaging Functional reserves, 102 Functional skills training, 66–69, 74t

Index G Gender-related factors, 373 General Health Policy Model, 228–229 General Health Questionnaire (GHQ), 192 Genetic factors depression and, 424 MCI/DAT and, 268 multiple sclerosis and, 360 vascular dementia and, 284–285 Glasgow Coma Scale (GCS) overview, 124–125, 303 traumatic brain injury and, 309 Glaucoma, automobile driving and, 170–171 Global Assessment of Functioning (GAF), 13 Global cognitive impairment, 11 Global Deficit Score, 11, 126 Global Deterioration Scale (GDS), 267 Goal management treatment (GMT) method, 80–81 Goal-directed activities, 63 “Goal–Plan–Do–Check” strategy, 66 Go/no-go decision making attentional functioning and, 177 automobile driving and, 173–175, 174t Group therapy, 365

H Halstead–Reitan Battery, 11 Hamilton Anxiety Rating Scale, 377 Hamilton Depression Rating Scale, 377 Headaches sports-related mild traumatic brain injury and, 334–335, 334t, 335t traumatic brain injury and, 309–310 Headminders, 342 Health and Activities Limitations Index (HALex), 227f Health beliefs model, 152–153 Health factors, automobile driving and, 192 Health literacy, 142 Health promotion, 53–54 Health Utilities Index (HUI), 227f Health-related quality of life Alzheimer’s disease and, 238–241, 239f, 242f measurement of, 225–230, 226f, 227t multiple sclerosis and, 375–377 neurocognitive dysfunction and, 230–238, 231t, 232f, 233f, 235f, 236f, 237f overview, 225, 241, 243 See also Quality of life Heart Failure Symptom Check List (HFSC), 227f Highly active antiretroviral therapy (HAART) health-related quality of life and, 230 medication adherence and, 145–146 neurocognitive impairment associated with HIV and, 393

469 overview, 389 survival times and, 396–397 Hippocampus memory and, 69 vascular dementia and, 284 HIV infection automobile driving and, 402–405, 403f, 404f ecologically oriented measures and, 25 future directions in, 409 health management and, 405–408, 406f health-related quality of life and, 230–237, 231t, 232f, 233f, 235f, 236f, 237f HIV-associated dementia (HAD), 392–393, 408 instrumental activities of daily living (IADLs) and, 8, 9, 102–103, 394–395, 395f medication management and, 145–150, 146f, 147f, 148f, 149f, 154–155, 397–399, 399f memory and, 24 neurocognitive impairment and, 392–393 overview, 389–392, 408 predicting vocational functioning and, 126, 128 quality of life and, 395–396, 396f risk behaviors and, 405–408, 406f survival times and, 396–397 vocational functioning and, 129, 399–402 HIV Neurobehavioral Research Center (HNRC), 230–231 HIV-associated dementia (HAD), 392–393, 408 Home Environmental Assessment Protocol (HEAP), 77, 77t Home Occupational Environmental Assessment (HOEA), 77, 77t Hooper Visual Organization Test, 288 Hopelessness feelings traumatic brain injury and, 319 vocational functioning and, 319 Hopkins Symptom Checklist, 240 Hopkins Verbal Learning Test (HVLT) automobile driving and, 271, 371 multiple sclerosis and, 371 Human Factors and Ergonomics Society (HFES), 40 Human factors/ergonomics (HF/E) applications of, 49–57 automobile driving and, 186–189, 187f developing solutions and, 47–49 future directions in, 57–58 medication adherence and, 143, 158 overview, 39–40, 40t question-asking process and, 41–49, 42t, 45f, 46t tools and techniques of, 43–47, 45f, 46t Human occupation model, 62. See also Person, environment, and occupation (PEO) approach Hypnotics, 183 Hypoglycemic agents, 183 Hypothalamic–pituitary–adrenal (HPA) axis, 424 Hypothetical model, 120–121, 121f

470 I Imaginary Processes Inventory, 379 ImPACT (Immediate Post-Concussion Assessment and Cognitive Testing), 342–344, 343t Impairment compared to disability, 119–120 distinction between function and ability, 14 Impulse control automobile driving and, 176–177 HIV infection and, 405–408, 406f instrumental activities of daily living (IADLs) and, 102–103 Inattentional amnesia, 178 Incapacity Status Scale (ISS), 366 Individual differences aging and, 248–249 future directions in assessment and, 460–461 sports-related mild traumatic brain injury and, 338–340 Informant report in assessment instrumental activities of daily living (IADLs) and, 96–98 overview, 13 Information-processing aging and, 253 automobile driving and, 169–170, 169f, 313 human factors/ergonomics (HF/E) and, 42t medication adherence and, 141, 142 multiple sclerosis and, 361–362 occupational therapy approach and, 65 traumatic brain injury and, 304–305, 306, 321 Inhibition, 252 Instrumental activities of daily living (IADLs) aging and, 251, 253–256, 257 assessment of, 96–105 clinical judgment and, 105 Cognitive Functional Evaluation (CFE) and, 72 cross-cultural assessment and, 215, 217–218 dementia and mild cognitive impairment (MCI) and, 268–269 depression and, 429 future directions in, 105–107 HIV infection and, 394–395, 395f human factors/ergonomics (HF/E) and, 49–51 memory and, 24 neuropsychological testing and, 101–105 occupational therapy approach and, 81 overview, 8–9, 93–96, 107–108 traumatic brain injury and, 307–310, 320–321 vascular dementia and, 288–296, 291t Instrumental Activities of Daily Living Scale (IADLS), 97 Instrumented vehicles, 186–189, 187f, 190f Intelligence aging and, 250–251 multiple sclerosis and, 362

Index International Classification of Functioning, Disability, and Health (ICF) construct validity and, 211–212 overview, 119 International Society for Pharmacoeconomics and Outcomes Research (ISPOR), 210 Interpersonal therapy (IPT), 423 Intervention studies, 79–81 Interventions automobile driving and, 191–192 medication adherence and, 156–158 Interviews, 43 Intuition, cultural differences in, 56 “Investigator-initiated” approach, 458 Iowa Gambling Task automobile driving and, 174t HIV infection and, 405–408, 406f IQ aging and, 250–251 multiple sclerosis and, 362 predicting vocational functioning and, 126 vocational functioning and, 9, 122–123

J Judgment of Line Orientation (JLO) automobile driving and, 193 multiple sclerosis and, 374, 375

K Kettle Test, 73, 74t Kinetic depth perception, 172 Kitchen Task Assessment (KTA) instrumental activities of daily living (IADLs), 100 overview, 73, 74t Klein–Bell ADL Scale, 366 Knowledge engineering human factors/ergonomics (HF/E) and, 47 vocational functioning and, 52 Knowledge-based errors, 176

L Laboratory-based measures, 139–140 Language barriers construct validity of assessment and, 212–213 cross-cultural assessment and, 210–211 Language functioning multiple sclerosis and, 362 vascular dementia and, 288 Large ACLS–5, 71 Learning factors depression and, 427–428 human factors/ergonomics (HF/E) and, 42t

Index multiple sclerosis and, 362 traumatic brain injury and, 305 Lesion localization, 5–6 Life-Satisfaction-9 questionnaire, 81 Linguistic functioning. See Language functioning Literacy levels, 18 Living alone, dementia and mild cognitive impairment (MCI) and, 270 Loewenstein Occupational Therapy Cognitive Assessment (LOTCA), 71t, 72 Logical memory, 288. See also Memory Long-term memory, 251–252. See also Memory LOTCA Geriatric Version (LOTCA-G), 72 Luria Frontal Lobe Syndrome Test (LFST), 376

M Macular degeneration, 170–171 Magnetic resonance imaging (MRI). See Neuroimaging Magnetoencephalography (MEG) data. See Neuroimaging Major depressive disorder (MDD). See Depression Management of medication. See Medication management Manifest functioning, 13–14 MATRICS (Measurement and Treatment Research to Improve Cognition in Schizophrenia) initiative future directions in assessment and, 458–459 overview, 449–450, 450t Mayo–Portland Adaptability Inventory (MPAI), 309 Measure of Awareness of Financial Skills (MAFS), 312 Medial temporal love, vascular dementia and, 284 Medical Outcomes Study 36-Item Short Form (SF36) overview, 226–227, 227f traumatic brain injury and, 309 Medication adherence dementia and mild cognitive impairment (MCI) and, 271–272 depression and, 430 future directions in, 158 human factors/ergonomics (HF/E) and, 53–54 interventions and, 156–158 multiple sclerosis and, 372–373 psychosocial models of, 152–156 schizophrenia and, 446 See also Medication management Medication Event Monitoring System (MEMS), 139 Medication management adherence methodologies, 137–140 aging and, 140–143 automobile driving and, 183 dementia and, 143–145 depression and, 430

471 future directions in, 158 HIV infection and, 145–150, 146f, 147f, 148f, 149f, 397–399, 399f human factors/ergonomics (HF/E) and, 53–54 interventions and, 156–158 multiple sclerosis and, 372–373 overview, 7–8, 136–137 psychiatric disorders and, 150–152 See also Medication adherence Medication Management Ability assessment (MMA) instrumental activities of daily living (IADLs) and, 100 medication adherence and, 139–140 Medication Management Test (MMT) HIV infection and, 398 medication adherence and, 139–140 Medication regime, 146–148, 147f, 148f, 149f Medications automobile driving and, 431–432 depression and, 183, 422–423, 431–432 effects of, 183 health-related quality of life and, 237–238 schizophrenia and, 442–444, 449–451, 450t Memory aging and, 251–252 automobile driving and, 177–179, 313 cognition and, 65 cognitive disabilities model and, 68–69 depression and, 427–428 functional skills training and, 67 health promotion and, 54 HIV infection and, 407–408 human factors/ergonomics (HF/E) and, 42t instrumental activities of daily living (IADLs) and, 308 medication adherence and, 140–142, 372–373 multiple sclerosis and, 361–362, 372–373, 374 neuropsychological testing and, 21, 24 predicting vocational functioning and, 127 selective serontonin reuptake inhibitors (SSRIs) and, 422–423 traumatic brain injury and, 305, 308 vascular dementia and, 287, 288, 291 Mental health, 304. See also Psychiatric disorders Mental Measurements Yearbook (MMY), 114 Mental retardation, 126 Mental status, 335t. See also Mini-Mental State Evaluation (MMSE) Metabolic processes, 333 Metacognition, automobile driving and, 179 Mild cognitive impairment (MCI) activities of daily living (ADLs) and, 268–269, 290 automobile driving and, 270–271 caregiver burden and, 272–273 clinical assessment of, 266–267 dementia of the Alzheimer’s type (DAT) and, 265–266

472 Mild cognitive impairment (cont.) diagnostic criteria for, 265 falls and, 270 future directions in, 273–274 living alone/wandering and, 270 medication adherence and, 271–272 neuropathology and genetics of, 268 vascular dementia and, 290 See also Dementia Mild traumatic brain injury, 338–339. See also Sports-related mild traumatic brain injury; Traumatic brain injury (TBI) Mini-Mental State Evaluation (MMSE) automobile driving and, 193 dementia and, 269 multiple sclerosis and, 377, 378 overview, 71, 71t schizophrenia and, 446 sexual dysfunction and, 378 vascular dementia and, 292 Minnesota Multiphasic Personality Inventory (MMPI) overview, 12–13 vocational functioning and, 122–123 Minor cognitive motor disorder (MCMD), 392 Minor neurocognitive disorder (MND), 392 Mobility, aging and, 255–256 Model of Human Ecology, 62. See also Person, environment, and occupation (PEO) approach Money management overview, 7–8 traumatic brain injury and, 310–313, 320–321 Money Management Survey (MMS), 311–312 Mood-congruent cognitive processing, 428–429 Motivation cultural differences in, 56 medication adherence and, 157 neuropsychological testing and, 17–18 Motor functioning, 141 Motor memory, 104–105. See also Memory Motor skills, predicting vocational functioning and, 126 MRI scan. See Neuroimaging MS Functional Composite (MSFC), 370 Multiple Errands Test, 23, 27, 76 Multiple sclerosis activities of daily living (ADLs) and, 366–368 automobile driving and, 10–11, 369–372 diagnosis of, 358–360 medication management/adherence and, 372–373 overview, 357–365, 379 sexual dysfunction and, 377–379 social functioning and quality of life and, 375–377 vocational functioning and, 126, 373–375 Multiple Sleep Latency Test (MSLT), 181 Myelin loss multiple sclerosis and, 358 vascular dementia and, 284

Index N National Aeronautics and Space Administration— Task Load Index (NASA-TLX), 44, 46, 46t National Eye Institute Visual Function Questionnaire–25 automobile driving and, 192 health-related quality of life and, 227f National Institute of Neurological Disorders and Stroke—Association Internationale pour la Recherche et l’Enseignement en Neurosciences (NINDS-AIREN), 283–284, 290, 292 National Institutes of Health (NIH), 458–459 Naturalistic Action Test, 23 Naturalistic driving, 189, 190f Navigational functioning, 54–56. see also Automobile driving Neurobehavioral Functioning Inventory (NFI), 309 Neurocognitive Driving Test (NDT), 369, 370 Neurocognitive dysfunction, health-related quality of life and, 230–238, 231t, 232f, 233f, 235f, 236f, 237f Neuroergonomics, 57. See also Human factors/ ergonomics (HF/E) Neurofunctional retraining, 66–67 Neuroimaging concussions and, 333 dementia of the Alzheimer’s type (DAT) and, 267 depression and, 424 future directions in assessment and, 460 HIV infection and, 391 sports-related mild traumatic brain injury and, 338, 345–347, 347f Neuropathology, 268 Neuropsychological abilities, 125–127 Neuropsychological Assessment Battery (NAB), 271 Neuropsychological testing case illustration of, 351–353, 352t defining “everyday functioning” outcomes and, 12–14 dementia and mild cognitive impairment (MCI) and, 266–267 distinction between function and ability, 14–19 ecological validity and, 20–25 future directions in, 26–28 instrumental activities of daily living (IADLs) and, 101–105, 309 limitations of, 103–105 measurement of vocational outcome, 116–117 overview, 11 predicting real-world behavior and, 25–26 predictive validity of, 114–116, 115t sports-related mild traumatic brain injury and, 337–338, 340–344, 343t, 351–353, 352t traumatic brain injury and, 309, 319–320, 321, 322t variables in, 19–20 vocational functioning and, 113–116, 115t, 319–320 See also Assessment

Index NIRVANA (Nissan–Iowa Instrumented Research Vehicle of Advanced Neuroergonomic Assessment), 186–189, 187f Nondeclarative memory. See Procedural memory Noradrenergic system, automobile driving and, 181 Nottingham Health Profile (NHP), 227f Numeric sense, medication adherence and, 142 Numerical reasoning, predicting vocational functioning and, 126

O Observations in assessment attentional functioning and, 21 instrumental activities of daily living (IADLs) and, 309 occupational therapy approach and, 64 overview, 14 translation factors and, 215, 217–218 traumatic brain injury and, 309 Observed Tasks of Daily Living—Revised (OTDL-R), 73 Obstructive sleep apnea syndrome (OSAS), 180–181 Occipital lobe lesions, 171 Occupational goal intervention (OGI), 80–81 Occupational Information Network (O*NET), 114 Occupational performance, 62 Occupational therapy approach assessment and, 69–78, 71t, 74t, 77t cognitive theoretical models and, 64–69, 71t, 74t intervention studies and, 79–81 overview, 62–64 Office of Population Censuses and Surveys (OPCS), 214 Older adult population, 8, 102–103. See also Aging Older Adults Resource Center Scale (OARS), 97 Oligodendrocytes, 284 100-Car Naturalistic Driving Study overview, 10–11, 14, 28 Oral Symbol Digit Modalities Test, 367, 374 Organization, 65

P Pain, traumatic brain injury and, 309–310 Pain medications, automobile driving and, 183 Parietal lobe, 171 Parkinson’s disease automobile driving and, 181 memory and, 24 neuropsychological testing and, 23 Part-task training, 47–48 PASAT automobile driving and, 370, 371–372 multiple sclerosis and, 367, 370, 371–372, 374, 375–376 vocational functioning and, 374

473 Patient’s Assessment of Own Functioning Inventory health-related quality of life and, 235–237, 236f overview, 12 Pediatric Activity Card Sort, 70 Perceptual impulsiveness, 176. See also Impulse control Perceptual processing, 170–172, 172f, 178 Performance Test of Activities of Daily Living (PADL), 98 Performance-based approach to assessment aging and, 254 instrumental activities of daily living (IADLs) and, 98–101 vocational functioning and, 128–130 Peripheral vision, 171 Person, environment, and occupation (PEO) approach, 62–64 Person analysis, human factors/ergonomics (HF/E) and, 41–49, 42t, 45f, 46t Personal data assistant (PDA) use, 48 Personality, 179–180 Personality disorders, 180 Person–environment–occupational performance model, 62. See also Person, environment, and occupation (PEO) approach PET scans. See Neuroimaging Pharmacy refill records, 139 Pill counts, 138 Polypharmacy, 157–158 Polysonography (PSG), 181 Positive and Negative Symptom Scale (PANSS), 127 Positron emission tomography (PET). See Neuroimaging Postacute brain injury, 8. See also Brain injury Posttraumatic amnesia (PTA) overview, 303 vocational functioning and, 125 Predictive validity of neuropsychological testing, 113–116, 115t Prefrontal cortex, vascular dementia and, 284 Prefrontal lesions, automobile driving and, 179 Premorbid functioning level, neuropsychological testing and, 19 Prigatano Competency Rating Scale (PCRS), 76 Problem solving cognitive orientation to daily occupational performance (CO-OP), 65–66 individualized approaches to, 17 Procedural learning, 104–105 Procedural memory aging and, 251–252 cognitive disabilities model and, 69 functional skills training and, 66–67 See also Memory Process diagrams, 43–44, 45f Processing speed, 11 Pro-Ex scale, 76

474 Prospective memory overview, 24 traumatic brain injury and, 305 See also Memory Proxy information in assessment, 13 Psuedodementia, depression and, 425 Psychiatric disorders medication management and, 150–152 neuropsychological testing and, 16–17 overview, 304 See also Depression; Schizophrenia Psychomotor functioning, 288 Psychopathology, traumatic brain injury and, 304 Psychopharmacological treatment depression and, 422–423, 426–427 schizophrenia and, 449–451, 450t See also Medications Psychosis automobile driving and, 180 depression and, 428 health-related quality of life and, 237–238 See also Schizophrenia Psychosocial models depression and, 423 medication adherence and, 152–156 overview, 126 psychosocial functioning and, 429–430 Public policy, automobile driving and, 192–195, 194t

Q Quality Enhancement by Strategic Teaming (QuEST), 431 Quality of life depression and, 432 HIV infection and, 395–396, 396f multiple sclerosis and, 375–377 overview, 225 See also Health-related quality of life Quality of Well-Being Scale (QWB) Alzheimer’s disease and, 238–241, 239f, 242f depression and, 432 health-related quality of life and, 227f overview, 228–230, 241, 243 Questionnaires, human factors/ergonomics (HF/E), 43

R Rating scales dementia and mild cognitive impairment (MCI) and, 267 traumatic brain injury and, 309 Reactive astrocytosis, vascular dementia and, 284 Reasoned action theory, 154 Reasoning skills aging and, 252 cognition and, 65 cultural differences in, 56

Index Recall deficits, 69 Recall of Recent Life Events, 21 Record review automobile driving and, 184 overview, 13–14 Redesign, human factors/ergonomics (HF/E) and, 48–49 Rehabilitation medication adherence and, 156 multiple sclerosis and, 367–368 traumatic brain injury and, 321 Relapse, multiple sclerosis and, 359–360 Remission depression and, 426 multiple sclerosis and, 359–360 Response inhibition, 176–177 Retinitis pigmentosa, automobile driving and, 170–171 Retrieval deficits, 69 Return-to-work analysis neuropsychological testing instruments that focus on, 21 overview, 9–10 Revised Observed Tasks of Daily Living (OTDL-R), 74t Rey Auditory Verbal Learning Test (AVLT) automobile driving and, 193 vascular dementia and, 287 Rey–Osterreith Complex Figure (ROCF), 271 Rey–Osterreith Complex Figure Test copy version (CFT-copy), 193 Rey–Osterrieth Figure Test, 106 Right-hemisphere stroke, 79–80 Risk behaviors, HIV infection and, 405–408, 406f Risk determination, automobile driving and, 169–170, 169f, 180, 192–195, 194t Rivermead Behavioral Memory Test (RBMT) instrumental activities of daily living (IADLs) and, 105 multiple sclerosis and, 367, 376 occupational therapy approach and, 74t overview, 21, 74 traumatic brain injury and, 306 Road rage, 180 Road tests, 183–184, 196 Routine Task Inventory (RTI-E), 72, 74t Rule-based errors, automobile driving and, 176 Russell Average Impairment Rating, 126

S Safety Assessment of Function and the Environment for Rehabilitation (SAFER), 77, 77t Sampling of behavior in assessment, 16 Schizophrenia automobile driving and, 180 cognitive functioning and, 442–443, 444–451, 447f, 450t depression and, 425

Index disability and outcome in, 443–444 health-related quality of life and, 237–238 instrumental activities of daily living (IADLs) and, 8 medication adherence and, 140, 151–152 medications and, 449–451, 450t memory and, 24 neuropsychological testing and, 16–17, 23 occupational therapy approach and, 80–81 outcomes associated with, 447–449, 447f overview, 441–443, 451 predicting vocational functioning and, 126–127 Seattle Longitudinal Study of Adult Intelligence, 250–251 Sedatives, automobile driving and, 183 Selective attention aging and, 253 traumatic brain injury and, 305 See also Attentional functioning Selective noradrenalin reuptake inhibitors (SNRIs) automobile driving and, 431–432 overview, 422–423 Selective Reminding Test (SRT), 374 Selective serontonin reuptake inhibitors (SSRIs) automobile driving and, 431–432 overview, 422–423 Self-awareness automobile driving and, 313 traumatic brain injury and, 306–307, 308, 319, 321 vocational functioning and, 319 Self-Awareness of Deficits Interview (SADI), 70 Self-efficacy, 153–154 Self-report measures aging and, 254 dementia and mild cognitive impairment (MCI) and, 267 fatigue and, 181 health-related quality of life and, 235–237, 236f instrumental activities of daily living (IADLs) and, 96–98 medication adherence and, 138 multiple sclerosis and, 378 overview, 12–13 sexual dysfunction and, 378 Sensorimotor functioning, 141 Sensoriperceptual memory, 68. See also Memory Sensory processing, automobile driving and, 170–172, 172f Sequential model, vocational functioning and, 120–121, 121f Serotonergic system, automobile driving and, 181 7/24 Spatial Recall Test, 367, 374 Sexual behavior, HIV infection and, 405–408, 406f Sexual functioning, multiple sclerosis and, 365, 377–379 SF-36 International Quality of Life Assessment ecological validity and, 214 multiple sclerosis and, 375–376 Shifting attention, 178–179. See also Attentional functioning

475 Short Blessed test, 71t Short-term memory, 251. See also Memory Sickness Impact Profile (SIP) health-related quality of life and, 227f traumatic brain injury and, 309 Significant others in assessment instrumental activities of daily living (IADLs) and, 96–98 overview, 13 Six Elements Test, 21, 23 Sleep deprivation, automobile driving and, 180–181 Sleep disorders, automobile driving and, 180–181 Social action theory, medication adherence and, 154–155 Social cognition, schizophrenia and, 448 Social determination framework, 153–154 Social functioning, multiple sclerosis and, 375–377 Social knowledge (schema), 448 Social perception, schizophrenia and, 448 Social support medication adherence and, 155 multiple sclerosis and depression and, 365 Solution development activities of daily living (ADLs) and, 51 automobile driving and, 55–56 health promotion and, 54 human factors/ergonomics (HF/E) and, 47–49 vocational functioning and, 52–53 Somatic marker hypothesis, 183 Spasticity, 371 Specificity, element of, 95–96 Specificity of neuropsychological tests, 15 Sports-related mild traumatic brain injury assessment and, 345–347, 347f case illustration of, 348–354, 352t epidemiology of, 332–333 neuropsychological testing and, 340–344, 343t on-field and sideline management of, 334–335, 334t, 335t overview, 331–332, 348 pathophysiology of, 333 returning to play and, 336–340, 336t, 338t, 344–345 See also Traumatic brain injury (TBI) St. Georges Respiratory Questionnaire (SGRQ), 227f State of California Alzheimer’s Disease Diagnostic and Treatment Centers (SCADDTC), 282t, 283–284 Strategy learning and awareness approach, 64–66 Stress, 365 Stress management training, 365 Striatum, 284 Stroke activities of daily living (ADLs) and, 288–296, 291t automobile driving and, 10–11 occupational therapy approach and, 81 pathophysiology of, 284–285 See also Vascular dementia Stroke Impact Scale (SIS), 81

476 Stroop Color and Word Test, 174t Stroop Color–Word Interference Test, 6 Structure from motion (SFM), 172 Structured Assessment of Independent Living Scales (SAILS), 99 Subcortical nuclei, 284 Subjective Workload Assessment Technique (SWAT), 44, 46 Substance use automobile driving and, 179–180, 183 HIV infection and, 405–408, 406f neuropsychological testing and, 16–17 traumatic brain injury and, 304 Suicide rates, traumatic brain injury and, 304 Surveys, human factors/ergonomics (HF/E) and, 43 Survival strategies, automobile driving and, 55–56 Symbol Digit Modalities Test automobile driving and, 371 multiple sclerosis and, 364, 371, 374, 375, 376 Symptoms of concussions, 334–335, 334t, 335t System characteristics, human factors/ergonomics (HF/E) and, 41–49, 42t, 45f, 46t Systems theory, neuropsychological testing and, 27

T Task analysis activities of daily living (ADLs) and, 50–51 automobile driving and, 54–55 health promotion and, 53–54 human factors/ergonomics (HF/E) and, 43–44, 45f Task demands, human factors/ergonomics (HF/E) and, 42t Task performance functional skills training and, 74t measures of cognition in, 72–74 Technology future directions in assessment and, 457–458 medication adherence and, 138–139 Temporal lobe, automobile driving and, 171 Test of Everyday Attention (TEA) multiple sclerosis and, 367 occupational therapy approach and, 74t overview, 21, 74 traumatic brain injury and, 306 Test of Everyday Functional Ability (TEFA) instrumental activities of daily living (IADLs) and, 100 Thalamus, vascular dementia and, 284 Theory of mind, schizophrenia and, 448 Therapy, multiple sclerosis and depression and, 365 Three-stage model of motor learning, 66 Tinkertoy Test, 21 Toglia Categorization Assessment (TCA), 74t, 75 Tower of Hanoi automobile driving and, 174t multiple sclerosis and, 374

Index Tower of London, 6, 75 Trail Making Test (Part A and/or B) automobile driving and, 174t, 192–193, 271, 371 instrumental activities of daily living (IADLs) and, 106 medication adherence and, 147, 148f multiple sclerosis and, 371 overview, 15, 19–20 predicting vocational functioning and, 127 vascular dementia and, 290–291 Training aging and, 256–257 health promotion and, 54 human factors/ergonomics (HF/E) and, 47–48 Training needs analysis, 47–48 Translation cross-cultural assessment and, 210–211, 213 example of, 215, 217–218 Traumatic brain injury (TBI) automobile driving and, 10–11, 313–317 cognitive functioning and, 304–307 instrumental activities of daily living (IADLs) and, 307–310 money management and, 310–313 overview, 302–307, 303t, 320–322, 322t pathophysiology of, 302–303 predicting vocational functioning and, 126 sexual dysfunction and, 379 vocational functioning and, 9–10, 113–114, 115–116, 121–122, 123–124, 317–320 See also Brain injury; Sports-related mild traumatic brain injury Treatment, depression and, 422–423, 426–427 Treatment adherence, multiple sclerosis and, 365 Treatment Units for Neurocognition and Schizophrenia (TURNS), 450–451 Tricyclic antidepressants, 422–423

U UCSD Performance-Based Skills Assessment (UPSA) health-related quality of life and, 238 instrumental activities of daily living (IADLs) and, 100 overview, 21 Unilateral spatial neglect (USN), 79–80 Usability testing, human factors/ergonomics (HF/E) and, 46–47 Useful Field of View (UFOV) test aging and, 255, 256 automobile driving and, 55, 56, 177, 192, 316, 404–405 ecologically oriented measures and, 25 HIV infection and, 404–405 instrumental activities of daily living (IADLs) and, 103

Index multiple sclerosis and, 369, 370 overview, 10–11 traumatic brain injury and, 316 User–system problems, human factors/ergonomics (HF/E) and, 41–49, 42t, 45f, 46t

V Variables in neurological testing, 19–20 Vascular dementia activities of daily living (ADLs) and, 288–296, 291t automobile driving and, 294–295 cognitive profile of, 287–288 diagnosis of, 281–284, 282t frequency of, 285–286 future directions in, 296–297 heterogeneous outcome of, 286–287 instrumental activities of daily living (IADLs) and, 8 overview, 280–281, 295–296 pathophysiology of, 284–285 See also Dementia; Stroke Verbal fluency deficits, 306 Verbal functioning, 362, 374 Verbal learning, 126, 128 Verbal memory, 126. See also Memory Vetromedial prefrontal cortex, depression and, 424 Visual functioning automobile driving and, 55, 170–172, 172f, 404–405 HIV infection and, 404–405 human factors/ergonomics (HF/E) and, 42t sports-related mild traumatic brain injury and, 335 vascular dementia and, 288 See also Visuospatial functioning Visual Motor Speed Composite, 345 Visual Spatial Search Task (VISSTA) occupational therapy approach and, 74t, 79–80 overview, 74–75 Visual working memory, 178. See also Memory Visuospatial functioning automobile driving and, 11, 193 cognitive disabilities model and, 68 multiple sclerosis and, 362 occupational therapy approach and, 79 See also Visual functioning Vocabulary skills, 126 Vocational functioning depression and, 430–431 future directions in, 130–132 health-related quality of life and, 232–234, 233f HIV infection and, 399–402 human factors/ergonomics (HF/E) and, 51–53 measurement of vocational outcome, 116–117 multiple sclerosis and, 373–375

477 neuropsychological studies and, 122–127 overview, 9–10, 113–116, 115t performance-based assessment of, 128–130 traumatic brain injury and, 317–320, 320–321 Vocational outcome measurement of, 116–117 neuropsychological studies and, 122–127 predicting independent of disease variables, 128 See also Vocational functioning

W Wandering, 270 Warning systems, automobile driving and, 191–192 Wechsler Adult Intelligence Scale, 106 Wechsler Adult Intelligence Scale—Revised (WAIS-R) automobile driving and, 193 multiple sclerosis and, 361 vocational functioning and, 125, 130 Wechsler Memory Scales, 107 White matter multiple sclerosis and, 358 vascular dementia and, 284 White matter hyperintensities, 285–286 Wisconsin Card Sorting Test (WCST) automobile driving and, 174t multiple sclerosis and, 363, 374, 375 overview, 75 predicting vocational functioning and, 127 Work Behavior Inventory, 127 Working memory automobile driving and, 177–179 cognitive disabilities model and, 68 health promotion and, 54 instrumental activities of daily living (IADLs) and, 102–103 medication adherence and, 141, 142, 372–373 multiple sclerosis and, 361–362, 372–373 See also Memory Workload analysis, 44, 46, 46t Workplace, 51–53 World Health Organization (WHO) construct validity and, 211–212 overview, 119

Y Years living with disability (YLDs), 421

Z Zone of proximal development, 75 Zoo Map, occupational therapy approach and, 80